Search results for: Deep mixed column

Authors: Sandeep Santosh, O. P. Sahu

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In this paper, performance comparison of wideband covariance matrix sparse representation (W-CMSR) method with other existing wideband Direction of Arrival (DOA) estimation methods has been made.W-CMSR relies less on a priori information of the incident signal number than the ordinary subspace based methods.Consider the perturbation free covariance matrix of the wideband array output. The diagonal covariance elements are contaminated by unknown noise variance. The covariance matrix of array output is conjugate symmetric i.e its upper right triangular elements can be represented by lower left triangular ones.As the main diagonal elements are contaminated by unknown noise variance,slide over them and align the lower left triangular elements column by column to obtain a measurement vector.Simulation results for W-CMSR are compared with simulation results of other wideband DOA estimation methods like Coherent signal subspace method (CSSM), Capon, l1-SVD, and JLZA-DOA. W-CMSR separate two signals very clearly and CSSM, Capon, L1-SVD and JLZA-DOA fail to separate two signals clearly and an amount of pseudo peaks exist in the spectrum of L1-SVD.

Keywords: W-CMSR, wideband direction of arrival (DOA), covariance matrix, electrical and computer engineering

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Authors: Robert van Ling, Alexander Schwahn, Shanhua Lin, Ken Cook, Frank Steiner, Rowan Moore, Mauro de Pra

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Purpose: Demonstration of fast high resolution charge variant analysis for monoclonal antibody (mAb) therapeutics within 5 minutes. Methods: Three commercially available mAbs were used for all experiments. The charge variants of therapeutic mAbs (Bevacizumab, Cetuximab, Infliximab, and Trastuzumab) are analyzed on a strong cation exchange column with a linear pH gradient separation method. The linear gradient from pH 5.6 to pH 10.2 is generated over time by running a linear pump gradient from 100% Thermo Scientific™ CX-1 pH Gradient Buffer A (pH 5.6) to 100% CX-1 pH Gradient Buffer B (pH 10.2), using the Thermo Scientific™ Vanquish™ UHPLC system. Results: The pH gradient method is generally applicable to monoclonal antibody charge variant analysis. In conjunction with state-of-the-art column and UHPLC technology, ultra fast high-resolution separations are consistently achieved in under 5 minutes for all mAbs analyzed. Conclusion: The linear pH gradient method is a platform method for mAb charge variant analysis. The linear pH gradient method can be easily optimized to improve separations and shorten cycle times. Ultra-fast charge variant separation is facilitated with UHPLC that complements, and in some instances outperforms CE approaches in terms of both resolution and throughput.

Keywords: charge variants, ion exchange chromatography, monoclonal antibody, UHPLC

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Authors: Xueming Xu, Jiahao Liu, Jichao Li, Kewei Yang, Minghao Li, Bingfeng Ge

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A combat system-of-systems (CSoS) comprises various types of functional combat entities that interact to meet corresponding task requirements in the present and future. Enhancing the resilience of CSoS holds significant military value in optimizing the operational planning process, improving military survivability, and ensuring the successful completion of operational tasks. Accordingly, this research proposes an integrated framework called CSoS resilience enhancement (CSoSRE) to enhance the resilience of CSoS from a recovery perspective. Specifically, this research presents a two-phase resilience optimization model to define a resilience optimization objective for CSoS. This model considers not only task baseline, recovery cost, and recovery time limit but also the characteristics of emergency recovery and comprehensive recovery. Moreover, the research extends it from the deterministic case to the stochastic case to describe the uncertainty in the recovery process. Based on this, a resilience-oriented recovery optimization method based on deep reinforcement learning (RRODRL) is proposed to determine a set of entities requiring restoration and their recovery sequence, thereby enhancing the resilience of CSoS. This method improves the deep Q-learning algorithm by designing a discount factor that adapts to changes in CSoS state at different phases, simultaneously considering the network’s structural and functional characteristics within CSoS. Finally, extensive experiments are conducted to test the feasibility, effectiveness and superiority of the proposed framework. The obtained results offer useful insights for guiding operational recovery activity and designing a more resilient CSoS.

Keywords: combat system-of-systems, resilience optimization model, recovery optimization method, deep reinforcement learning, certainty and uncertainty

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

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Authors: Nan Deng, Zhenqiu Liu

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Thousands of patients with metastatic tumors were diagnosed with cancers of unknown primary sites each year. The inability to identify the primary cancer site may lead to inappropriate treatment and unexpected prognosis. Nowadays, a large amount of genomics and transcriptomics cancer data has been generated by next-generation sequencing (NGS) technologies, and The Cancer Genome Atlas (TCGA) database has accrued thousands of human cancer tumors and healthy controls, which provides an abundance of resource to differentiate cancer types. Meanwhile, deep convolutional neural networks (CNNs) have shown high accuracy on classification among a large number of image object categories. Here, we utilize 25 cancer primary tumors and 3 normal tissues from TCGA and convert their RNA-Seq gene expression profiling to color images; train, validate and test a CNN classifier directly from these images. The performance result shows that our CNN classifier can archive >80% test accuracy on most of the tumors and normal tissues. Since the gene expression pattern of distant metastases is similar to their primary tumors, the CNN classifier may provide a potential computational strategy on identifying the unknown primary origin of metastatic cancer in order to plan appropriate treatment for patients.

Keywords: bioinformatics, cancer, convolutional neural network, deep leaning, gene expression pattern

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Authors: D. Naresh Yadav, K. Anand Kishore, Bhaskar Bethi, Shirish H. Sonawane, D. Bhagawan

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The untreated industrial wastewater discharged into the environment causes the contamination of soil, water and air. Advanced treatment methods for enhanced wastewater treatment are attracting substantial interest among the currently employed unit processes in wastewater treatment. The textile industry is one of the predominant in wastewater production at current industrialized situation. The refused dyes at textile industry need to be treated in proper manner before its discharge into water bodies. In the present investigation, hybrid treatment process has been developed for the treatment of synthetic mixed dye wastewater. Photocatalysis and ceramic nanoporous membrane are mainly used for process integration to minimize the fouling and increase the flux. Commercial semiconducting powders (TiO2 and ZnO) has used as a nano photocatalyst for the degradation of mixed dye in the hybrid system. Commercial ceramic nanoporous tubular membranes have been used for the rejection of dye and suspended catalysts. Photocatalysis with catalyst has shown the average of 34% of decolorization (RB-32%, BG-34% and CR-36%), whereas ceramic nanofiltration has shown the 56% (RB-54%, BG-56% and CR-58%) of decolorization. Integration of photocatalysis and ceramic nanofiltration has shown 96% (RB-94%, BG-96% and CR-98%) of dye decolorization over 90 min of operation.

Keywords: photocatalysis, ceramic nanoporous membrane, wastewater treatment, advanced oxidation process, process integration

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Authors: Barham J. Nareeman, Ilham I. Mohammed

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Excavations in urban developed area are generally supported by deep excavation walls such as; diaphragm wall, bored piles, soldier piles and sheet piles. In some cases, these walls may be braced by internal braces or tie back anchors. Tie back anchors are by far the predominant method for wall support, the large working space inside the excavation provided by a tieback anchor system has a significant construction advantage. This paper aims to analyze a deep excavation bracing system of contiguous pile wall braced by pre-stressed tie back anchors, which is a part of a huge residential building project, located in Turkey/Gaziantep province. The contiguous pile wall will be constructed with a length of 270 m that consists of 285 piles, each having a diameter of 80 cm, and a center to center spacing of 95 cm. The deformation analysis was carried out by a finite element analysis tool using PLAXIS. In the analysis, beam element method together with an elastic perfect plastic soil model and Soil Hardening Model was used to design the contiguous pile wall, the tieback anchor system, and the soil. The two soil clusters which are limestone and a filled soil were modelled with both Hardening soil and Mohr Coulomb models. According to the basic design, both soil clusters are modelled as drained condition. The simulation results show that the maximum horizontal movement of the walls and the maximum settlement of the ground are convenient with 300 individual case histories which are ranging between 1.2mm and 2.3mm for walls, and 15mm and 6.5mm for the settlements. It was concluded that tied-back contiguous pile wall can be satisfactorily modelled using Hardening soil model.

Keywords: deep excavation, finite element, pre-stressed tie back anchors, contiguous pile wall, PLAXIS, horizontal deflection, ground settlement

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Authors: Guanghua Zhang, Fubao Wang, Weijun Duan

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Convolution neural network (CNN) has attracted more and more attention on recent years. Especially in the field of computer vision and image classification. However, unsupervised learning with CNN has received less attention than supervised learning. In this work, we use a new powerful tool which is deep convolutional generative adversarial networks (DCGANs) to generate images from Sloan Digital Sky Survey. Training by various star and galaxy images, it shows that both the generator and the discriminator are good for unsupervised learning. In this paper, we also took several experiments to choose the best value for hyper-parameters and which could help to stabilize the training process and promise a good quality of the output.

Keywords: convolution neural network, discriminator, generator, unsupervised learning

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

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Authors: S. Khurram, N. Mubashar

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The paper examines, through the lens of Postcolonial Theory, how natives resist and react in Derrek Walcott’s “Dream on Monkey Mountain”. It aims at how natives, for being ‘recognized and reformed’, mimic and adapt the white’s ways of living. It also focuses how Walcott expresses natives’ reaction when they cannot construct their identity. Moreover, the paper exploits the Homi. K Bhaba’s concept of Mimicry and Berry’s concepts of Hybridity to explain Caribbean native’s plight. Furthermore, it bring forth Walcott’s deep insight into the psychology of the Caribbean natives. He digs deep into the colonial discourse to reconstruct post-colonial identity and he, as a post-colonial writer, does so by deconstructing colonial ideology of racism by resisting against it.

Keywords: postcolonial theory, mimicry, hybridity, reaction

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Authors: Andrés I. Ávila, Patricia Aros, César San Martín, Elizabeth Kehr, Yovana Leal

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We consider a linear programming model to help farmers to decide if it is convinient to choose among three kinds of export fruits for their future investment. We consider area, investment, water, productivitiy minimal unit, and harvest restrictions and a monthly based model to compute the average income in five years. Also, conditions on the field as area, water availability and initia investment are required. Using the Chilean costs and dollar-peso exchange rate, we can simulate several scenarios to understand the possible risks associated to this market.

Keywords: mixed integer problem, fruit production, support decision model, fruit tree farms

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Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

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In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy.

Keywords: autoencoder, brainwave signal analysis, electroencephalogram, feature extraction, feature selection, optimization

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Authors: Hussain Jiffry, Kypros Pilakoutas, Reyes Garcia Lopez

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This study examines the effect of tsunami loads on reinforced concrete (RC) frame buildings analytically. The impact of tsunami wave loads and waterborne objects are analyzed using a typical substandard full-scale two-story RC frame building tested as part of the EU-funded Ecoleader project. The building was subjected to shake table tests in bare condition and subsequently strengthened using Carbon Fiber Reinforced Polymers (CFRP) composites and retested. Numerical models of the building in both bare and CFRP-strengthened conditions are calibrated in DRAIN-3DX software to match the test results. To investigate the response of wave loads and impact forces, the numerical models are subjected to nonlinear dynamic analyses using force-time history input records. The analytical results are compared in terms of displacements at the floors and the 'impact point' of a boat. The results show that the roof displacement of the CFRP-strengthened building reduced by 63% when compared to the bare building. The results also indicate that strengthening only the mid-height of the impact column using CFRP is more efficient at reducing damage when compared to strengthening other parts of the column. Alternative solutions to mitigate damage due to tsunami loads are suggested.

Keywords: tsunami loads, hydrodynamic load, impact load, waterborne objects, RC buildings

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Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: deep learning network, smart metering, water end use, water-energy data

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Bambang Antoro, Lasito Soebari, Geoffrey de Jong, Fernandy Meiriyanto, Michael Siahaan, Eko Wibowo, Pormando Silalahi, Ruswanto, Adi Budirumantyo

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The Ertsberg East Skarn System (EESS) is located in the Ertsberg Mining District, Papua, Indonesia. EESS is a sub-vertical zone of copper-gold mineralization hosted in both diorite (vein-style mineralization) and skarn (disseminated and vein style mineralization). Deep Mill Level Zone (DMLZ) is a mining zone in the lower part of East Ertsberg Skarn System (EESS) that product copper and gold. The Deep Mill Level Zone deposit is located below the Deep Ore Zone deposit between the 3125m to 2590m elevation, measures roughly 1,200m in length and is between 350 and 500m in width. DMLZ planned start mined on Q2-2015, being mined at an ore extraction rate about 60,000 tpd by the block cave mine method (the block cave contain 516 Mt). Mineralization and associated hydrothermal alteration in the DMLZ is hosted and enclosed by a large stock (The Main Ertsberg Intrusion) that is barren on all sides and above the DMLZ. Late porphyry dikes that cut through the Main Ertsberg Intrusion are spatially associated with the center of the DMLZ hydrothermal system. DMLZ orebody hosted in diorite and skarn, both dominantly by vein style mineralization. Percentage Material Mined at DMLZ compare with current Reserves are diorite 46% (with 0.46% Cu; 0.56 ppm Au; and 0.83% EqCu); Skarn is 39% (with 1.4% Cu; 0.95 ppm Au; and 2.05% EqCu); Hornfels is 8% (with 0.84% Cu; 0.82 ppm Au; and 1.39% EqCu); and Marble 7 % possible mined waste. Correlation between Ertsberg intrusion, major structure, and vein style mineralization is important to determine characteristic orebody in DMLZ Mine. Generally Deep Mill Level Zone has 2 type of vein filling mineralization from both hosted (diorite and skarn), in diorite hosted the vein system filled by chalcopyrite-bornite-quartz and pyrite, in skarn hosted the vein filled by chalcopyrite-bornite-pyrite and magnetite without quartz. Based on orientation the stockwork vein at diorite hosted and shallow vein in skarn hosted was generally NW-SE trending and NE-SW trending with shallow-moderate dipping. Deep Mill Level Zone control by two main major faults, geologist founded and verified local structure between major structure with NW-SE trending and NE-SW trending with characteristics slickenside, shearing, gauge, water-gas channel, and some has been re-healed.

Keywords: copper-gold, DMLZ, skarn, structure

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Authors: Mingjun Xu, Shouxiang Lu

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Understanding the interaction mechanism between the water droplet and pool fire has an important significance in engineering application of water sprinkle/spray/mist fire suppression. The micro impact process is unclear when the droplet impacts on the burning liquid surface at present. To deepen the understanding of the mechanisms of pool fire suppression with water spray/mist, dynamic processes of single water droplet impinging onto a hot heptane surface are visualized with the aid of a high-speed digital camera at 2000 fps. Each test is repeated 20 times. The water droplet diameter is around 1.98 mm, and the impact Weber number ranges from 30 to 695. The heptane is heated by a hot plate to mimic the burning condition, and the temperature varies from 30 to 90°C. The results show that three typical phenomena, including penetration, crater-jet and surface bubble, are observed, and the pool temperature has a significant influence on the critical condition for the appearance of each phenomenon. A global picture of different phenomena is built according to impact Weber number and pool temperature. In addition, the pool temperature and Weber number have important influences on the characteristic parameters including maximum crater depth, crown height and liquid column height. For a fixed Weber number, the liquid column height increases with pool temperature.

Keywords: droplet impact, fire suppression, hot surface, water spray

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Authors: Kanate Pantusavase, Jaramporn Hassamontr

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This research aims to develop an algorithm to generate a schedule of multiple cranes that will maximize load throughputs in anodizing operation. The algorithm proposed utilizes an enumerative strategy to search for constant time between successive loads and crane covering range over baths. The computer program developed is able to generate a near-optimal crane schedule within reasonable times, i.e. within 10 minutes. Its results are compared with existing solutions from an aluminum extrusion industry. The program can be used to generate crane schedules for mixed products, thus allowing mixed-model line balancing to improve overall cycle times.

Keywords: crane scheduling, anodizing operations, cycle time minimization

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Authors: Khalid S. Hashim, Andy Shaw, Rafid Alkhaddar

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Dissolved oxygen concentration (DO) plays a key role in the electrocoagulation process (EC) as it oxidizes the heavy metals, ammonia, and cyanide into other forms that can be removed easily from water. For instance, the DO oxidises Fe (II) to Fe (III), As (III) to As (V), and cyanide to cyanate and then to ammonia. As well as, removal of nitrogenous compounds accomplishes by the presence of DO. Hence, many of the previous investigations used external aerators to provide the required DO inside EC reactors especially when the water being treated has low DO (such as leachate and highly polluted waters with organic matter); or when the DO depleted during the EC treatment. Although the external aeration process effectively enhances the DO concentration, it has a significant impact on energy consumption. Where, the presence of air bubbles increases the electrical resistance of the EC cell that increase the energy consumption in consequence. Thus, the present project aims to fill this gap by an innovative use of perforated flow columns in the designing of a new EC reactor (ECR1). The new reactor (ECR1) consisted of a Perspex made cylinder container having a controllable working volume of 0.5 to 1 L. It supplied with a flow column that consisted of perorated discoid electrodes that made from aluminium. In order to investigate the performance of ECR1; water samples with a controlled DO concentration were pumped at different flow rates (110, 220, and 440 ml/min) to the ECR1 for 10 min. The obtained results demonstrated that the ECR1 increased the DO concentration from 5.0 to 9.54, 10.53, and 11.0 mg/L which equivalent to 90.8%, 110.6%, and 120% at flow rates of 110, 220, and 440 mL/min respectively.

Keywords: dissolved oxygen, flow column, electrocoagulation, aluminium electrodes

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Authors: Tesfaw Belayneh Abebe

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Integrating low-altitude earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) within a non-terrestrial network (NTN) with the assistance of reconfigurable intelligent surfaces (RIS), we investigate the problem of how to enhance throughput through integrated LEO satellites and UAVs with the assistance of RIS. We propose a method to jointly optimize the associations with the LEO satellite, the 3D trajectory of the UAV, and the phase shifts of the RIS to maximize communication throughput for RIS-assisted integrated LEO satellite and UAV-enabled wireless communications, which is challenging due to the time-varying changes in the position of the LEO satellite, the high mobility of UAVs, an enormous number of possible control actions, and also the large number of RIS elements. Utilizing a multi-agent double deep Q-network (MADDQN), our approach dynamically adjusts LEO satellite association, UAV positioning, and RIS phase shifts. Simulation results demonstrate that our method significantly outperforms baseline strategies in maximizing throughput. Lastly, thanks to the integrated network and the RIS, the proposed scheme achieves up to 65.66x higher peak throughput and 25.09x higher worst-case throughput.

Keywords: integrating low-altitude earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs) within a non-terrestrial network (NTN), reconfigurable intelligent surfaces (RIS), multi-agent double deep Q-network (MADDQN)

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Authors: Chien-Kuo Chang, Yu-Hsiang Lin, Yi-Yun Tang, Min-Chiu Wu

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With the trend of technological advancement, the harm caused by power outages is substantial, mostly due to problems in the power grid. This highlights the necessity for further improvement in the reliability of the power system. In the power system, gas-insulated switches (GIS) and power cables play a crucial role. Long-term operation under high voltage can cause insulation materials in the equipment to crack, potentially leading to partial discharges. If these partial discharges (PD) can be analyzed, preventative maintenance and replacement of equipment can be carried out, there by improving the reliability of the power grid. This research will diagnose defects by identifying three different defects in GIS and three different defects in straight cable joints, for a total of six types of defects. The partial discharge data measured will be converted through phase analysis diagrams and pulse sequence analysis. Discharge features will be extracted using convolutional image processing, and three different deep learning models, CNN, ResNet18, and MobileNet, will be used for training and evaluation. Class Activation Mapping will be utilized to interpret the black-box problem of deep learning models, with each model achieving an accuracy rate of over 95%. Lastly, the overall model performance will be enhanced through an ensemble learning voting method.

Keywords: partial discharge, gas-insulated switches, straight cable joint, defect identification, deep learning, ensemble learning

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Authors: Chien-kuo Chang, Bo-wei Wu, Yi-yun Tang, Min-chiu Wu

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This paper proposes a power equipment diagnosis system based on partial discharge (PD), which is characterized by increasing the readability of experimental data and the convenience of operation. This system integrates a variety of analysis programs of different data formats and different programming languages and then establishes a set of interfaces that can follow and expand the structure, which is also helpful for subsequent maintenance and innovation. This study shows a case of using the developed Convolutional Neural Networks (CNN) to integrate with this system, using the designed model architecture to simplify the complex training process. It is expected that the simplified training process can be used to establish an adaptive deep learning experimental structure. By selecting different test data for repeated training, the accuracy of the identification system can be enhanced. On this platform, the measurement status and partial discharge pattern of each equipment can be checked in real time, and the function of real-time identification can be set, and various training models can be used to carry out real-time partial discharge insulation defect identification and insulation state diagnosis. When the electric power equipment entering the dangerous period, replace equipment early to avoid unexpected electrical accidents.

Keywords: partial discharge, convolutional neural network, partial discharge analysis platform, adaptive deep learning

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Authors: Musa Najimu, Isam Aljundi

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In this study, the development of mixed matrix membrane (MMM) containing carbide-derived carbon (CDC) for the separation of CO₂ was investigated. MMM with four different loadings (0.1 to 2 wt%) were prepared by the dry/wet phase inversion technique. Prior to this, the formula of the control polysulfone (PSF) membrane was optimized in terms of the PSF concentration in a mixture of NMP/THF solvents and ethanol. Prepared samples were characterized and tested for CO₂ and CH₄ gas permeation. The optimization of the control PSF membrane revealed that 30 wt% PSF is the critical polymer concentration in the formulation. Characterization results unveiled reinforcement of thermal stability and improved polarity imparted by CDC in the MMM, in addition to uniform dispersion of filler up to 1 wt% loading. Furthermore, the incorporation of CDC in PSF membrane formulation enhanced both the CO₂ permeance and ideal selectivity over the control membrane. A CDC loading of 0.5 wt% resulted in the highest CO₂ permeance of 5.5 GPU corresponding to 120% increase in permeance while a CDC loading of 1 wt% resulted in the highest selectivity (CO₂ /CH₄) of 27 corresponding to 29% increase in selectivity. Studies of operating temperature effect showed that an optimum operating temperature for M1.0 membrane is 20 ⁰C. In addition, the feed pressure studies showed that high pressure feeds will favor high performance of the membrane and a good CO₂ /CH₄ separation.

Keywords: carbide derived carbon, mixed matrix membrane, CO₂ separation, polysulfone

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Authors: Rewati Raman, Uttam K. Roy

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One of the main adversaries of city planning in present times is the ever-expanding problem of urbanization and the antagonistic issues accompanying it. The prevalent challenges in urbanization such as population growth, urban sprawl, poverty, inequality, pollution, congestion, etc. call for reforms in the urban fabric as well as in planning theory and practice. One of the various paradigms of city planning, land use planning, has been the major instruments for spatial planning of cities and regions in India. Zoning regulation based land use planning in the form of land use and development control plans (LUDCP) and development control regulations (DCR) have been considered mainstream guiding principles in land use planning for decades. In spite of many advantages of such zoning based regulations, over a period of time, it has been critiqued by scholars for its own limitations of isolation and lack of vitality, inconvenience in business in terms of proximity to residence and low operating cost, unsuitable environment for small investments, higher travel distance for facilities, amenities and thereby higher expenditure, safety issues etc. Mixed land use has been advocated as a tool to avoid such limitations in city planning by researchers. In addition, mixed land use can offer many advantages like housing variety and density, the creation of an economic blend of compatible land use, compact development, stronger neighborhood character, walkability, and generation of jobs, etc. Alternatively, the mixed land use beyond a suitable balance of use can also bring disadvantages like traffic congestion, encroachments, very high-density housing leading to a slum like condition, parking spill out, non-residential uses operating on residential premises paying less tax, chaos hampering residential privacy, pressure on existing infrastructure facilities, etc. This research aims at studying and outlining the various challenges and potentials of mixed land use zoning, through modeling tools, as a competent instrument for city planning in lieu of the present urban scenario. The methodology of research adopted in this paper involves the study of a mixed land use neighborhood in India, identification of indicators and parameters related to its extent and spatial pattern and the subsequent use of system dynamics as a modeling tool for simulation. The findings from this analysis helped in identifying the various advantages and challenges associated with the dynamic nature of a mixed use urban settlement. The results also confirmed the hypothesis that mixed use neighborhoods are catalysts for employment generation, socioeconomic gains while improving vibrancy, health, safety, and security. It is also seen that certain challenges related to chaos, lack of privacy and pollution prevail in mixed use neighborhoods, which can be mitigated by varying the percentage of mixing as per need, ensuring compatibility of adjoining use, institutional interventions in the form of policies, neighborhood micro-climatic interventions, etc. Therefore this paper gives a consolidated and holistic framework and quantified outcome pertaining to the extent and spatial pattern of mixed land use that should be adopted to ensure sustainable urban planning.

Keywords: mixed land use, sustainable development, system dynamics analysis, urban dynamics modelling

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Authors: Christopher Baiyegunhi, Oswald Gwavava

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The location/source of the Beattie magnetic anomaly (BMA) and interconnectivity of geologic structures at depth have been a topic of investigation for over 30 years. Up to now, no relationship between geological structures (interconnectivity of dolerite intrusions) at depth has been established. Therefore, the environmental impact of fracking the Karoo for shale gas could not be assessed despite the fact that dolerite dykes are groundwater localizers in the Karoo. In this paper, we shed more light to the unanswered questions concerning the possible location of the source of the BMA, the connectivity of geologic structures like dolerite dykes and sills at depth and this relationship needs to be established before the tectonic evolution of the Karoo basin can be fully understood and related to fracking of the Karoo for shale gas. The result of the magnetic investigation and modelling of four gravity profiles that crosses the BMA in the study area reveals that the anomaly, which is part of the Beattie magnetic anomaly tends to divide into two anomalies and continue to trend in an NE-SW direction, the dominant gravity signatures is of long wavelength that is due to a deep source/interface inland and shallows towards the coast, the average depth to the top of the shallow and deep magnetic sources was estimated to be approximately 0.6 km and 15 km, respectively. The BMA become stronger with depth which could be an indication that the source(s) is deep possibly a buried body in the basement. The bean-shaped anomaly also behaves in a similar manner like the BMA thus it could possibly share the same source(s) with the BMA.

Keywords: Beattie magnetic anomaly, magnetic sources, modelling, Karoo Basin

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Authors: Hasan Yalcin, Tugba Dursun Capar

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Deep-fat frying causes desirable as well as undesirable changes in oil and potato, and changes the quality of the oil by hydrolysis, oxidation, and polymerization. The main objective of the present study was to investigate the pre-drying effects on the quality of both frying oil and potatoes. Prior to frying, potato slices (10 mm x10 mm x 30 mm) were air- dried at 60°C for 15, 30, 45, 60, 90, and 120 mins., respectively. Potato slices without the pre-drying treatment were considered as the control variable. Potato slices were fried in sunflower oil at 180°C for 5, 10, and 13 mins. The deep-frying experiments were repeated five times using the new potato slices in the same oil without oil replenishment. Samples of the fresh oil, together with those sampled at the end of successive frying operations (1th, 3th and 5th) were removed and analysed. Moisture content, colour and oil intake of the potato and colour, peroxide value (PV), free fatty acid (FFA), fatty acid composition and viscosity of the used oil were evaluated. The effect of frying time was also examined. Results show that pre-drying treatment had a significant effect on physicochemical properties and colour parameters of potato slices and frying oil. Pre-drying considerably decreased the oil absorption. The lowest oil absorption was found for the treatment that was pre-dried for 120, and fried for 5 min. The FFA levels decreased permanently for each pre-treatment throughout the frying period. All the pre-drying treatments had reached their maximum levels of FFA by the end of the frying procedures. The PV of the control and 60 min pre-dried sample decreased after the third frying. However, the PV of other samples increased constantly throughout the frying periods. Lastly, pre-drying did not affect the fatty acid composition of frying oil considerably when compared against previously unused oil.

Keywords: air-drying, deep-fat frying, moisture content oil uptake, quality

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Authors: Mohamed Y. M. Mohsen

Abstract:

The viability of utilising mixed-oxide fuel (MOX) ((U₀.₉, rgPu₀.₁) O₂) as an accident-tolerant fuel (ATF) has been thoroughly investigated. MOX fuel provides the best example of a nuclear waste recycling process. The MCNPX 2.7 code was used to determine the main neutronic features, especially the radial power distribution, to identify the hot channel on which the thermal-hydraulic (TH) study was performed. Based on the computational fluid dynamics technique, the simulation of the rod-centered thermal-hydraulic subchannel model was implemented using COMSOL Multiphysics. TH analysis was utilised to determine the axially and radially distributed temperatures of the fuel and cladding materials, as well as the departure from the nucleate boiling ratio (DNBR) along the coolant channel. COMSOL Multiphysics can simulate reality by coupling multiphysics, such as coupling between heat transfer and solid mechanics. The main solid structure parameters, such as the von Mises stress, volumetric strain, and displacement, were simulated using this coupling. When the neutronic, TH, and solid structure performances of UO₂ and ((U₀.₉, rgPu₀.₁) O₂) were compared, the results showed considerable improvement and an increase in safety margins with the use of ((U₀.₉, rgPu₀.₁) O₂).

Keywords: mixed-oxide, MCNPX, neutronic analysis, COMSOL-multiphysics, thermal-hydraulic, solid structure

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Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

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Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator

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Authors: Parisa Mansour

Abstract:

The coronavirus disease 2019 (COVID-19) was discovered and rapidly spread to various countries around the world since the end of 2019. Computed tomography (CT) images have been used as an important alternative to the time-consuming RT. PCR test. However, manual segmentation of CT images alone is a major challenge as the number of suspected cases increases. Thus, accurate and automatic segmentation of COVID-19 infections is urgently needed. Because the imaging features of the COVID-19 infection are different and similar to the background, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to build a deep convolutional neural network adapted for the segmentation of chest CT images with COVID-19 infections. First, we maintain a large and novel chest CT image database containing 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of an infected lung can be improved by global intensity adjustment, we introduce a feature variable block into the proposed deep CNN, which adjusts the global features of features to segment the COVID-19 infection. The proposed PV array can effectively and adaptively improve the performance of functions in different cases. We combine features of different scales by proposing a progressive atrocious space pyramid fusion scheme to deal with advanced infection regions with various aspects and shapes. We conducted experiments on data collected in China and Germany and showed that the proposed deep CNN can effectively produce impressive performance.

Keywords: chest, COVID-19, chest Image, coronavirus, CT image, chest CT

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Authors: Carlos A. Vega-Posada, Jeisson Alejandro Higuita-Villa, Julio C. Saldarriaga-Molina

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This paper presents a simplified analytical approach to conduct elastic stability and second-order lateral stiffness analyses of beam-column elements (i.e., piles) with generalized end-boundary conditions embedded on a homogeneous or non-homogeneous Pasternak foundation. The solution is derived using the well-known Differential Transformation Method (DTM), and it consists simply of solving a system of two linear algebraic equations. Using other conventional approaches to solve the governing differential equation of the proposed element can be cumbersome and the solution challenging to implement, especially when the non-homogeneity of the soil is considered. The proposed formulation includes the effects of i) any rotational or lateral transverse spring at the ends of the pile, ii) any external transverse load acting along the pile, iii) soil non-homogeneity, and iv) the second-parameter of the elastic foundation (i.e., shear layer connecting the springs at the top). A parametric study is conducted to investigate the effects of different modulus of subgrade reactions, degrees of non-homogeneities, and intermediate end-boundary conditions on the pile response. The same set of equations can be used to conduct both elastic stability and static analyses. Comprehensive examples are presented to show the simplicity and practicability of the proposed method.

Keywords: elastic stability, second-order lateral stiffness, soil-non-homogeneity, pile analysis

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