Search results for: low input farming

Authors: Mojtaba Heydarizad

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Zagros mountain range is a very important precipitation zone in Iran as it receives high average annual precipitation compared to other parts of this country. Although this region is important precipitation zone in semi-arid an arid country like Iran, accurate method to study water resources in this region has not been applied yet. In this study, stable isotope δ¹⁸O content of precipitation and groundwater resources showed spatial variations across Zagros region as southern parts of Zagros region showed more enriched isotope values compared to the northern parts. This is normal as southern Zagros region is much drier with higher air temperature and evaporation compared to northern parts. In addition, the spatial variations of stable isotope δ¹⁸O in precipitation in Zagros region have been simulated by the models which consider the altitude and latitude variations as input to simulate δ¹⁸O in precipitation.

Keywords: groundwater, precipitation, simulation, stable isotopes, Zagros region

Procedia PDF Downloads 138

Authors: Yadpiroon Onmek, Jean Triboulet, Sebastien Druon, Bruno Jouvencel

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The objective of this paper is to develop the 3D underwater reconstruction of archaeology object, which is based on the fusion between a sonar system and stereo camera system. The underwater images are obtained from a calibrated camera system. The multiples image pairs are input, and we first solve the problem of image processing by applying the well-known filter, therefore to improve the quality of underwater images. The features of interest between image pairs are selected by well-known methods: a FAST detector and FLANN descriptor. Subsequently, the RANSAC method is applied to reject outlier points. The putative inliers are matched by triangulation to produce the local sparse point clouds in 3D space, using a pinhole camera model and Euclidean distance estimation. The SFM technique is used to carry out the global sparse point clouds. Finally, the ICP method is used to fusion the sonar information with the stereo model. The final 3D models have a précised by measurement comparing with the real object.

Keywords: 3D reconstruction, archaeology, fusion, stereo system, sonar system, underwater

Procedia PDF Downloads 299

Authors: Deepak

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WEDM is an amazingly potential electro-wire process for machining of hard metal compounds and metal grid composites without making contact. Wire electrical machining is a developing noncustomary machining process for machining hard to machine materials that are electrically conductive. It is an exceptionally exact, precise, and one of the most famous machining forms in nontraditional machining. WEDM has turned into the fundamental piece of many assembling process ventures, which require precision, variety, and accuracy. In the present examination, AA-6063 is utilized as a workpiece, and execution investigation is done to discover the critical control factors. Impact of different parameters like a pulse on time, pulse off time, servo voltage, peak current, water pressure, wire tension, wire feed upon surface hardness has been researched while machining on AA-6063. RSM has been utilized to advance the yield variable. A variety of execution measures with input factors was demonstrated by utilizing the response surface methodology.

Keywords: AA-6063, response surface methodology, WEDM, surface roughness

Procedia PDF Downloads 116

Authors: Yunwei Zhang, Qiaochu Tang, Yao Zhang, Jiabin Wang, Ulrich Stimming, Alpha Lee

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Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS) -- a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis -- with Gaussian process machine learning. We collect over 20,000 EIS spectra of commercial Li-ion batteries at different states of health, states of charge and temperatures -- the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems.

Keywords: battery degradation, machine learning method, electrochemical impedance spectroscopy, battery diagnosis

Procedia PDF Downloads 148

Authors: Pipop Chatrabhuti, S. Visessri, T. Charinpanitkul

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Precision irrigation (PI) technology has emerged as a solution to optimize water usage in agriculture, aiming to maximize crop yields while minimizing water waste. Developing a new PI for commercialization requires developers to research, synthesize, evaluate, and select appropriate technologies and make use of such information to produce innovative products. The objective of this review is to facilitate innovators by providing them with a summary of existing knowledge and the identification of gaps in research linking to the innovative development of PI. This paper reviews and synthesizes technologies and components relevant to precision irrigation, highlighting its potential benefits and challenges. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework is used for the review. The study is intended to contribute to innovators who apply for collaborative approach to problem-solving and idea generation that involves seeking external input and resources from a diverse range of individuals and organizations.

Keywords: innovation synthesis, technology assessment, precision irrigation technologies, precision irrigation components, new product development

Procedia PDF Downloads 65

Authors: M. Doumi, K. Tahir, A. Miloudi, A. G. Aissaoui, C. Belfedal, S. Tahir

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This paper presents a three-phase grid-connected photovoltaic generation system with unity power factor for any situation of solar radiation based on voltage-oriented control (VOC). An input voltage clamping technique is proposed to control the power between the grid and photovoltaic system, where it is intended to achieve the maximum power point operation. This method uses a Perturb and Observe (P&O) controller. The main objective of this work is to compare the energy production unit performances by the use of two types of controllers (namely, classical PI and Sliding Mode (SM) Controllers) for the grid inverter control. The proposed control has a hierarchical structure with a grid side control level to regulate the power (PQ) and the current injected to the grid and to obtain a common DC voltage constant. To show the effectiveness of both control methods performances analysis of the system are analyzed and compared by simulation and results included in this paper.

Keywords: grid connected photovoltaic, MPPT, inverter control, classical PI, sliding mode, DC voltage constant, voltage-oriented control, VOC

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Authors: S. M. Anowarul Haque, Md. Asiful Islam

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Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

Procedia PDF Downloads 171

Authors: Payal Patel

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Textile machinery is one of the fastest evolving areas which has an application of mechanical engineering. The modular approach towards the processing right from the stage of cotton to the fabric, allows us to observe the result of each process on its input. Cost and space being the major constraints. The flyer is a component of roving machine, which is used as a part of spinning process. In the present work using the application of Hyper Works, the flyer arm has been modified which saves the material used for manufacturing the flyer. The size optimization of the flyer is carried out with the objective of reduction in weight under the constraints of standard operating conditions. The new design of the flyer is proposed and validated using the module of HyperWorks which is equally strong, but light weighted compared to the existing design. Dynamic balancing of the optimized model is carried out to align a principal inertia axis with the geometric axis of rotation. For the balanced geometry of flyer, air resistance is obtained theoretically and with Gambit and Fluent. Static analysis of the balanced geometry has been done to verify the constraint of operating condition. Comparison of weight, deflection, and factor of safety has been made for different aluminum alloys.

Keywords: flyer, size optimization, textile, weight

Procedia PDF Downloads 215

Authors: Shibo Wei, Ting Jiang

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Convolutional-recurrent neural networks (CRN) have achieved much success recently in the speech enhancement field. The common processing method is to use the convolution layer to compress the feature space by multiple upsampling and then model the compressed features with the LSTM layer. At last, the enhanced speech is obtained by deconvolution operation to integrate the global information of the speech sequence. However, the feature space compression process may cause the loss of information, so we propose to model the upsampling result of each step with the residual LSTM layer, then join it with the output of the deconvolution layer and input them to the next deconvolution layer, by this way, we want to integrate the global information of speech sequence better. The experimental results show the network model (RES-CRN) we introduce can achieve better performance than LSTM without residual and overlaying LSTM simply in the original CRN in terms of scale-invariant signal-to-distortion ratio (SI-SNR), speech quality (PESQ), and intelligibility (STOI).

Keywords: convolutional-recurrent neural networks, speech enhancement, residual LSTM, SI-SNR

Procedia PDF Downloads 200

Authors: Hany Mohamed Abdu, Mohamed Hassan Gadallah, El-Giushi Mokhtar, Yehia Mahmoud Ismail

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Laser beam cutting is one nontraditional machining process. This paper optimizes the parameters of Laser beam cutting machining parameters of Stainless steel (316L) by considering the effect of input parameters viz. power, oxygen pressure, frequency and cutting speed. Statistical design of experiments are carried in three different levels and process responses such as 'Average kerf taper (Ta)' and 'Surface Roughness (Ra)' are measured accordingly. A quadratic mathematical model (RSM) for each of the responses is developed as a function of the process parameters. Responses predicted by the models (as per Taguchi’s L27 OA) are employed to search for an optimal parametric combination to achieve desired yield of the process. RSM models are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA) using MATLAB environment. Optimum solutions are compared with Taguchi Methodology results.

Keywords: optimization, laser cutting, robust design, kerf width, Taguchi method, RSM and DOE

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Authors: Carolina Zambrana, Grover Zurita

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The food loss production that occurs in deficient agricultural production is one of the major problems worldwide. This puts the population's food security and the efficiency of farming investments at risk. It is to be expected that this food security will be achieved with the own and efficient production of each country. It will have an impact on the well-being of its population and, thus, also on food sovereignty. The production losses in quantity and quality occur due to the lack of efficient detection of diseases at an early stage. It is very difficult to solve the agriculture efficiency using traditional methods since it takes a long time to be carried out due to detection imprecision of the main diseases, especially when the production areas are extensive. Therefore, the main objective of this research study is to perform a systematic literature review, of the latest five years, of Precision Agriculture (PA) to be able to understand the state of the art of the set of new technologies, procedures, and optimization processes with Artificial Intelligence (AI). This study will focus on Corns, Potatoes, and Rice diagnostic diseases. The extensive literature review will be performed on Elsevier, Scopus, and IEEE databases. In addition, this research will focus on advanced digital imaging processing and the development of software and hardware for PA. The convolution neural network will be handling special attention due to its outstanding diagnostic results. Moreover, the studied data will be incorporated with artificial intelligence algorithms for the automatic diagnosis of crop quality. Finally, precision agriculture with technology applied to the agricultural sector allows the land to be exploited efficiently. This system requires sensors, drones, data acquisition cards, and global positioning systems. This research seeks to merge different areas of science, control engineering, electronics, digital image processing, and artificial intelligence for the development, in the near future, of a low-cost image measurement system that allows the optimization of crops with AI.

Keywords: precision agriculture, convolutional neural network, deep learning, artificial intelligence

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Authors: Mohammad Arif Khan

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This paper analyzes and presents the development of Artificial Neural Network based controller of space vector modulation (ANN-SVPWM) for a seven-phase voltage source inverter. At first, the conventional method of producing sinusoidal output voltage by utilizing six active and one zero space vectors are used to synthesize the input reference, is elaborated and then new PWM scheme called Artificial Neural Network Based PWM is presented. The ANN based controller has the advantage of the very fast implementation and analyzing the algorithms and avoids the direct computation of trigonometric and non-linear functions. The ANN controller uses the individual training strategy with the fixed weight and supervised models. A computer simulation program has been developed using Matlab/Simulink together with the neural network toolbox for training the ANN-controller. A comparison of the proposed scheme with the conventional scheme is presented based on various performance indices. Extensive Simulation results are provided to validate the findings.

Keywords: space vector PWM, total harmonic distortion, seven-phase, voltage source inverter, multi-phase, artificial neural network

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Authors: Pinar Ozdemir Ayber, Zeina Hojeij

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Blended Learning (BL) is a rapidly growing teaching and learning approach, which brings together the best of both face-to-face and online learning to expand learning opportunities for students. However, there is limited research on the practices, opportunities and quality of instruction in Blended Classrooms, and on the role of the teaching faculty as well as the learners in these types of classes. This paper will highlight the researchers’ experiences and reflections on blending their classes. It will focus on the importance of designing effective lesson plans that emphasize learner engagement and motivation in alignment with course learning outcomes. In addition, it will identify the changing roles of the teacher and the learners and suggest appropriate variations to the traditional classroom setting taking into consideration the benefits and the challenges of the Blended Classroom. It is hoped that this paper would provide sufficient input for participants to reflect on ways they can blend their own lessons to promote ubiquitous learning and student autonomy. Practical tips and ideas will be shared with the participants on various strategies and technologies that were used in the researchers’ classes.

Keywords: blended learning, learner autonomy, learner engagement, learner motivation, mobile learning tools

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Authors: Yusuf Ziya Ayık, Ferhat Kahveci

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Currently, database management systems have various tools such as backup and maintenance, and also provide statistical information such as resource usage and security. In terms of query performance, this paper covers query optimization, views, indexed tables, pre-computation materialized view, query performance analysis in which query plan alternatives can be created and the least costly one selected to optimize a query. Indexes and views can be created for related table columns. The literature review of this study showed that, in the course of time, despite the growing capabilities of the database management system, only database administrators are aware of the need for dealing with archival and transactional data types differently. These data may be constantly changing data used in everyday life, and also may be from the completed questionnaire whose data input was completed. For both types of data, the database uses its capabilities; but as shown in the findings section, instead of repeating similar heavy calculations which are carrying out same results with the same query over a survey results, using materialized view results can be in a more simple way. In this study, this performance difference was observed quantitatively considering the cost of the query.

Keywords: cost of query, database management systems, materialized view, query performance

Procedia PDF Downloads 280

Authors: John Lachapelle

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Introduction: Neural stimulation is increasingly targeted toward treatment of back pain, PTSD, Parkinson’s disease, and for sensory perception. Sensory recording during stimulation is important in order to examine neural response to stimulation. Most neural amplifiers (headstages) focus on noise efficiency factor (NEF). Conversely, neural headstages need to handle artifacts from several sources including power lines, movement (EMG), and neural stimulation itself. In this work a layered approach to artifact rejection is used to reduce corruption of the neural ENG signal by 60dBv, resulting in recovery of sensory signals in rats and primates that would previously not be possible. Methods: The approach combines analog techniques to reduce and handle unwanted signal amplitudes. The methods include optimized (1) sensory electrode placement, (2) amplifier configuration, and (3) artifact blanking when necessary. The techniques together are like concentric moats protecting a castle; only the wanted neural signal can penetrate. There are two conditions in which the headstage operates: unwanted artifact < 50mV, linear operation, and artifact > 50mV, fast-settle gain reduction signal limiting (covered in more detail in a separate paper). Unwanted Signals at the headstage input: Consider: (a) EMG signals are by nature < 10mV. (b) 60 Hz power line signals may be > 50mV with poor electrode cable conditions; with careful routing much of the signal is common to both reference and active electrode and rejected in the differential amplifier with <50mV remaining. (c) An unwanted (to the neural recorder) stimulation signal is attenuated from stimulation to sensory electrode. The voltage seen at the sensory electrode can be modeled Φ_m=I_o/4πσr. For a 1 mA stimulation signal, with 1 cm spacing between electrodes, the signal is <20mV at the headstage. Headstage ASIC design: The front end ASIC design is designed to produce < 1% THD at 50mV input; 50 times higher than typical headstage ASICs, with no increase in noise floor. This requires careful balance of amplifier stages in the headstage ASIC, as well as consideration of the electrodes effect on noise. The ASIC is designed to allow extremely small signal extraction on low impedance (< 10kohm) electrodes with configuration of the headstage ASIC noise floor to < 700nV/rt-Hz. Smaller high impedance electrodes (> 100kohm) are typically located closer to neural sources and transduce higher amplitude signals (> 10uV); the ASIC low-power mode conserves power with 2uV/rt-Hz noise. Findings: The enhanced neural processing ASIC has been compared with a commercial neural recording amplifier IC. Chronically implanted primates at MGH demonstrated the presence of commercial neural amplifier saturation as a result of large environmental artifacts. The enhanced artifact suppression headstage ASIC, in the same setup, was able to recover and process the wanted neural signal separately from the suppressed unwanted artifacts. Separately, the enhanced artifact suppression headstage ASIC was able to separate sensory neural signals from unwanted artifacts in mouse-implanted peripheral intrafascicular electrodes. Conclusion: Optimizing headstage ASICs allow observation of neural signals in the presence of large artifacts that will be present in real-life implanted applications, and are targeted toward human implantation in the DARPA HAPTIX program.

Keywords: ASIC, biosensors, biomedical signal processing, biomedical sensors

Procedia PDF Downloads 330

Authors: Alyona Gencheva

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The most common way to study diagnostic results is using specialized programs at a stationary workplace. Magnetic Resonance Imaging is presented in a two-dimensional (2D) format, and Computed Tomography sometimes looks like a three-dimensional (3D) model that can be interacted with. The main idea of the research is to compare ways of displaying diagnostic results in virtual reality that can help a surgeon during or before an operation in augmented reality. During the experiment, the medical staff examined liver vessels in the abdominal area and heart boundaries. The search time and detection accuracy were measured on black-and-white and coloured scans. Usability testing in virtual reality shows convenient ways of interaction like hand input, voice activation, displaying risk to the patient, and the required number of scans. The results of the experiment will be used in the new C# program based on Magic Leap technology.

Keywords: augmented reality, computed tomography, magic leap, magnetic resonance imaging, usability testing, VTE risk

Procedia PDF Downloads 112

Authors: Miroslav Dumbrovsky

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The paper deals with impacts of climate change with the main emphasis on land degradation, agriculture and forestry management in the landscape. Land degradation, due to adverse effect of farmers activities, as a result of inappropriate conventional technologies, was a major issue in the Czech Republic during the 20th century and will remain for solving in the 21st century. The importance of land degradation is very high because of its impact on crop productivity and many other adverse effects. Land degradation through soil degradation is causing losses on crop productivity and quality of the environment, through decreasing quality of soil and water (especially water resources). Negative effects of conventional farming practices are increased water erosion, as well as crusting and compaction of the topsoil and subsoil. Soil erosion caused by water destructs the soil’s structure, reduces crop productivity due to deterioration in soil physical and chemical properties such as infiltration rate, water-holding capacity, loss of nutrients needed for crop production, and loss of soil carbon. Water erosion occurs on fields with row crops (maize, sunflower), especially during the rainfall period from April to October. Recently there is a serious problem of greatly expanded production of biofuels and bioenergy from field crops. The result is accelerated soil degradation. The damages (on and off- site) are greater than the benefits. An effective soil conservation requires an appropriate complex system of measures in the landscape. They are also important to continue to develop new sophisticated methods and technologies for decreasing land degradation. The system of soil conservation solving land degradation depend on the ability and the willingness of land users to apply them. When we talk about land degradation, it is not just a technical issue but also an economic and political issue. From a technical point of view, we have already made many positive steps, but for successful solving the problem of land degradation is necessary to develop suitable economic and political tools to increase the willingness and ability of land users to adopt conservation measures.

Keywords: land degradation, soil erosion, soil conservation, climate change

Procedia PDF Downloads 375

Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: C. Bunsanit

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This paper presents the refinement method for two beams formation of wideband smart antenna. The refinement method for weighting coefficients is based on Fully Spatial Signal Processing by taking Inverse Discrete Fourier Transform (IDFT), and its simulation results are presented using MATLAB. The radiation pattern is created by multiplying the incoming signal with real weights and then summing them together. These real weighting coefficients are computed by IDFT method; however, the range of weight values is relatively wide. Therefore, for reducing this range, the refinement method is used. The radiation pattern concerns with five input parameters to control. These parameters are maximum weighting coefficient, wideband signal, direction of mainbeam, beamwidth, and maximum of minor lobe level. Comparison of the obtained simulation results between using refinement method and taking only IDFT shows that the refinement method works well for wideband two beams formation.

Keywords: fully spatial signal processing, beam forming, refinement method, smart antenna, weighting coefficient, wideband

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Authors: Abdullahi Yahaya Musa, Harun Rann Bakari

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The study examined the determinants of food security among households in Maiduguri, Metropolis, Borno State, Nigeria. The objectives of the study are to: examine the determinants of food security among households; identify the coping strategies employed by food-insecure households in Maiduguri, Metropolis, Borno State, Nigeria. The population of the study is 843,964 respondents out of which 400 respondents were sampled. The study used a self-developed questionnaire to collect data from four hundred (400) respondents. Four hundred (400) copies of questionnaires were administered and all were retrieved, making 100% return rate. The study employed descriptive and inferential statistics for data analysis. Descriptive statistics (frequency counts and percentages) was used to analyze the socio-economic characteristics of the respondents and objective four, while inferential statistics (logit regression analysis) was used to analyze one. Four hundred (400) copies of questionnaires were administered and all the four hundred (400) were retrieved, making a 100% return rate. The results were presented in tables and discussed according to the research objectives. The study revealed that HHA, HHE, HHSZ, HHSX, HHAS, HHI, HHFS, HHFE, HHAC and HHCDR were the determinants of food security in Maiduguri Metropolis. Relying on less preferred foods, purchasing food on credit, limiting food intake to ensure children get enough, borrowing money to buy foodstuffs, relying on help from relatives or friends outside the household, adult family members skipping or reducing a meal because of insufficient finances and ration money to household members to buy street food were the coping strategies employed by food-insecure households in Maiduguri metropolis. The study recommended that Nigeria Government should intensify the fight against the Boko haram insurgency. This will put an end to Boko Haram Insurgency and enable farmers to return to farming in Borno state.

Keywords: internally displaced persons, food security, coping strategies, descriptive statistics, logistics regression model, odd ratio

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Authors: Zhaofeng Li, Jun Kang Chow, Yu-Hsing Wang

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This paper attempts to bridge the soil properties and the mechanical response of soil in the discrete element method (DEM) simulation. The artificial neural network (ANN) was therefore adopted, aiming to reproduce the stress-strain-volumetric response when soil properties are given. 31 biaxial shearing tests with varying soil parameters (e.g., initial void ratio and interparticle friction coefficient) were generated using the DEM simulations. Based on these 45 sets of training data, a three-layer neural network was established which can output the entire stress-strain-volumetric curve during the shearing process from the input soil parameters. Beyond the training data, 2 additional sets of data were generated to examine the validity of the network, and the stress-strain-volumetric curves for both cases were well reproduced using this network. Overall, the ANN was found promising in predicting the soil behavior and reducing repetitive simulation work.

Keywords: artificial neural network, discrete element method, soil properties, stress-strain-volumetric response

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Authors: A. Sathya, C. Manojkumar, D. Visithra

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The South Indian peninsula has rich diversity of both heritage and conventional rice varieties. With the prime focus set on high yield and increased productivity, a number of traditional/heritage rice varieties have dwindled into the forgotten past. At present, in the face of climate change, the hybrids and conventional varieties struggle for sustainable yield. The need of copious irrigation and high nutrient inputs for the hybrids and conventional varieties have cornered the farming and research community to resort to heritage rice varieties for their sturdy survival capability. An ethno-scientific effort has been taken in the Cauvery delta tracts of South India to restore these traditional/heritage rice varieties. A closer field level performance evaluation under organic condition has been undertaken for 10 heritage rice varieties. The morpho-agronomic characterization across vegetative and reproductive stages have revealed a pattern of variation in duration, plant height, number of tillers, productive tillers, etc. The shortest duration was recorded for a variety with the vernacular name of ‘Arubadaam kuruvai’. A traditional rice variety called ‘Maapillai samba’ is claimed to impart instant energy. The supernatant water of the overnight soaked cooked rice of Maapillai samba is a source of instant energy. The physico-chemical analysis of this variety is being explored for its instant nutritional boosting ability. Wide spectrum of nutritional characters including palatability and marketability preferences has also been analyzed for all these 10 heritage rice varieties. A ‘Farmer’s harvest day festival’ was organized, providing opportunity for the ‘Cauvery delta farmers’ to identify the special features and exchange their views on these standing golden ripe paddy varieties directly. The airing of their ethnic knowledge pooled with interesting scientific investigations of these 10 rich heritage rice varieties of South India undertaken will be elaborately discussed enlightening the perspectives on the pathway of resurrection and restoration of this heritage of the past.

Keywords: biodiversity, conservation, heritage, rice, traditional, varieties

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Authors: Khaled R. Khater

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The weight of the SPT hammer is standard (0.623kN). The locally manufacturer drilling rigs use hammers, sometimes deviating off the standard weight. This affects the field measured blow counts (Nf) consequentially, affecting most of correlations previously obtained, as they were obtained based on standard hammer weight. The literature presents energy corrections factor (η2) to be applied to the SPT total input energy. This research investigates the effect of the hammer weight variation, as a single parameter, on the field measured blow counts (Nf). The outcome is a correction factor (ηk), equation, and correction chart. They are recommended to adjust back the measured misleading (Nf) to the standard one as if the standard hammer is used. This correction is very important to be done in such cases where a non-standard hammer is being used because the bore logs in any geotechnical report should contain true and representative values (Nf), let alone the long records of correlations, already in hand. The study here-in is achieved by using laboratory physical model to simulate the SPT dripping hammer mechanism. It is designed to allow different hammer weights to be used. Also, it is manufactured to avoid and eliminate the energy loss sources. This produces a transmitted efficiency up to 100%.

Keywords: correction factors, hammer weight, physical model, standard penetration test

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Authors: Ian A. McCulloh, Madelaine Zinser, Jesse Patsolic, Michael Ramos

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Recommender systems are a common artificial intelligence (AI) application. For any given input, a search system will return a rank-ordered list of similar items. As users review returned items, they must decide when to halt the search and either revise search terms or conclude their requirement is novel with no similar items in the database. We present a statistically designed experiment that investigates the impact of similarity ratings on human judgement to conclude a search item is novel and halt the search. 450 participants were recruited from Amazon Mechanical Turk to render judgement across 12 decision tasks. We find the inclusion of ratings increases the human perception that items are novel. Percent similarity increases novelty discernment when compared with star-rated similarity or the absence of a rating. Ratings reduce the time to decide and improve decision confidence. This suggests the inclusion of similarity ratings can aid human decision-makers in knowledge search tasks.

Keywords: ratings, rankings, crowdsourcing, empirical studies, user studies, similarity measures, human-centered computing, novelty in information retrieval

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Authors: Yavuz Yardım

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The most important criteria for structural engineering is the structure’s ability to carry intended loads safely. The key element of this ability is mathematical modeling of really loadings situation into a simple loads input to use in structure analysis and design. Amongst many different types of loads, the most challenging load is earthquake load. It is possible magnitude is unclear and timing is unknown. Therefore the concept of intended loads and safety have been built on experience of previous earthquake impact on the structures. Understanding and developing these concepts is achieved by investigating performance of the structures after real earthquakes. Damage after an earthquake provide results of thousands of full-scale structure test under a real seismic load. Thus, Earthquakes reveille all the weakness, mistakes and deficiencies of analysis, design rules and practice. This study deals with lesson learnt from earthquake recoded last two decades in Turkey. Results of investigation after several earthquakes exposes many deficiencies in structural detailing, inappropriate design, wrong architecture layout, and mainly mistake in construction practice.

Keywords: earthquake, seismic assessment, RC buildings, building performance

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Authors: Wisdom Robert Duruji

Abstract:

Local farmers and fruit processors in developing countries of West Africa use different ripening agents to accelerate the ripening process of plantain and banana. This study reports on the influence of different ripening agents on the shelf-life and microbial load of organic and inorganic plantain (Musa paradisiaca) and banana (Musa sapientum) during ripening process and the health implication for food security in Nigeria. The experiment consisted of four treatments, namely: Calcium carbide, Irvingia gabonensis fruits, Newbouldia laevis leaves and a control, where no ripening agent was applied to the fingers of plantain and banana. The unripe and ripened plantain and banana were subjected to microbial analysis by isolating their micro flora (Bacteria, Yeast and Mould) using pour plate method. Microbes present in the samples were enumerated, characterized and classified to genera and species. The result indicated that the microbial load of inorganic plantain from (Urban day) open market in Ile-Ife increased from 8.00 for unripe to 12.11 cfu/g for ripened; and the microbial load of organic plantain from Obafemi Awolowo University Teaching and Research Farm (OAUTRF) increased from 6.00 for unripe to 11.60 cfu/g for ripened. Also, the microbial load of inorganic banana from (Urban day) open market in Ile-Ife increased from 8.00 for unripe to 11.50 cfu/g for ripened; while the microbial load of organic banana from OAUTRF increased from 6.50 for unripe to 9.40 cfu/g for ripened. The microbial effects of the ripening agents increased from 10.00 for control to 16.00 cfu/g for treated (ripened) organic and inorganic plantain; while that of organic and inorganic banana increased from 7.50 for control to 14.50 cfu/g for ripened. Visual observation for the presence of fungal colonies and deterioration rates were monitored till seven days after the plantain and banana fingers have fully ripened. Inorganic plantain and banana from (Urban day) open market in Ile-Ife are more contaminated than organic plantain and banana fingers from OAUTRF. The ripening accelerators reduced the shelf life, increased senescence, and microbial load of plantain and banana. This study concluded that organic Agriculture is better and microbial friendlier than inorganic farming.

Keywords: organic agriculture, food security, Musaceae, calcium carbide, Irvingia gabonensis, Newbouldia laevis

Procedia PDF Downloads 584

Authors: I. Gusti A. A. Rai Asmiwyati, Robert J. Corner, Ashraf M. Dewan

Abstract:

Change on land use/cover (LULC) dominantly affects spatial structure and function. It can have such impacts by disrupting social culture practice and disturbing physical elements. Thus, it has become essential to understand of the dynamics in time and space of LULC as it can be used as a critical input for developing sustainable LULC. This study was an attempt to map and monitor the LULC change in Bali Indonesia from 2003 to 2013. Using object based classification to improve the accuracy, and change detection, multi temporal land use/cover data were extracted from a set of ASTER satellite image. The overall accuracies of the classification maps of 2003 and 2013 were 86.99% and 80.36%, respectively. Built up area and paddy field were the dominant type of land use/cover in both years. Patch increase dominantly in 2003 illustrated the rapid paddy field fragmentation and the huge occurring transformation. This approach is new for the case of diverse urban features of Bali that has been growing fast and increased the classification accuracy than the manual pixel based classification.

Keywords: land use/cover, urban, Bali, ASTER

Procedia PDF Downloads 541

Authors: Anahita Farhang Ghahfarokhi, Alessandro Berti, Wil M.P. van der Aalst

Abstract:

Process mining provides ways to analyze business processes. Common process mining techniques consider the process as a whole. However, in real-life business processes, different behaviors exist that make the overall process too complex to interpret. Process comparison is a branch of process mining that isolates different behaviors of the process from each other by using process cubes. Process cubes organize event data using different dimensions. Each cell contains a set of events that can be used as an input to apply process mining techniques. Existing work on process cubes assume single case notions. However, in real processes, several case notions (e.g., order, item, package, etc.) are intertwined. Object-centric process mining is a new branch of process mining addressing multiple case notions in a process. To make a bridge between object-centric process mining and process comparison, we propose a process cube framework, which supports process cube operations such as slice and dice on object-centric event logs. To facilitate the comparison, the framework is integrated with several object-centric process discovery approaches.

Keywords: multidimensional process mining, mMulti-perspective business processes, OLAP, process cubes, process discovery, process mining

Procedia PDF Downloads 255

Authors: M. Kaouane, A. Boukhelifa, A. Cheriti

Abstract:

In this paper, a new Buck-Boost DC-DC converter is designed and simulated for photovoltaic energy system. The presented Buck-Boost converter has a double switch. Moreover, its output voltage is regulated to a constant value whatever its input is. In the presented work, the Buck-Boost transfers the produced energy from the photovoltaic generator to an R-L load. The converter is controlled by the pulse width modulation technique in a way to have a suitable output voltage, in the other hand, to carry the generator’s power, and put it close to the maximum possible power that can be generated by introducing the right duty cycle of the pulse width modulation signals that control the switches of the converter; each component and each parameter of the proposed circuit is well calculated using the equations that describe each operating mode of the converter. The proposed configuration of Buck-Boost converter has been simulated in Matlab/Simulink environment; the simulation results show that it is a good choice to take in order to maintain the output voltage constant while ensuring a good energy transfer.

Keywords: Buck-Boost converter, switch, photovoltaic, PWM, power, energy transfer

Procedia PDF Downloads 905

Authors: Khuram Shahzad, Omar Usman Khan

Abstract:

Quantum bit string comparators (QBSC) operate on two sequences of n-qubits, enabling the determination of their relationships, such as equality, greater than, or less than. This is analogous to the way conditional statements are used in programming languages. Consequently, QBSCs play a crucial role in various algorithms that can be executed or adapted for quantum computers. The development of efficient and generalized comparators for any n-qubit length has long posed a challenge, as they have a high-cost footprint and lead to quantum delays. Comparators that are efficient are associated with inputs of fixed length. As a result, comparators without a generalized circuit cannot be employed at a higher level, though they are well-suited for problems with limited size requirements. In this paper, we introduce a generalized design for the comparison of two n-qubit logic states using just two ancillary bits. The design is examined on the basis of qubit requirements, ancillary bit usage, quantum cost, quantum delay, gate operations, and circuit complexity and is tested comprehensively on various input lengths. The work allows for sufficient flexibility in the design of quantum algorithms, which can accelerate quantum algorithm development.

Keywords: quantum comparator, quantum algorithm, space-efficient comparator, comparator

Procedia PDF Downloads 15