Search results for: matrix minimization algorithm

Authors: Anthony D. Rhodes, Manan Goel

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We provide a high fidelity deep learning algorithm (HyperSeg) for interactive video segmentation tasks using a dense convolutional network with context-aware skip connections and compressed, 'hypercolumn' image features combined with a convolutional tessellation procedure. In order to maintain high output fidelity, our model crucially processes and renders all image features in high resolution, without utilizing downsampling or pooling procedures. We maintain this consistent, high grade fidelity efficiently in our model chiefly through two means: (1) we use a statistically-principled, tensor decomposition procedure to modulate the number of hypercolumn features and (2) we render these features in their native resolution using a convolutional tessellation technique. For improved pixel-level segmentation results, we introduce a boundary loss function; for improved temporal coherence in video data, we include temporal image information in our model. Through experiments, we demonstrate the improved accuracy of our model against baseline models for interactive segmentation tasks using high resolution video data. We also introduce a benchmark video segmentation dataset, the VFX Segmentation Dataset, which contains over 27,046 high resolution video frames, including green screen and various composited scenes with corresponding, hand-crafted, pixel-level segmentations. Our work presents a improves state of the art segmentation fidelity with high resolution data and can be used across a broad range of application domains, including VFX pipelines and medical imaging disciplines.

Keywords: computer vision, object segmentation, interactive segmentation, model compression

Procedia PDF Downloads 118

Authors: Shadan Dwairi

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Defining proper requirements is one of the key factors for a successful construction projects. Although there have been many attempts put forward in assist in identifying requirements, but still this area is under developed. In Buildings Information Modelling (BIM) projects. The Employer Information Requirements (EIR) is the fundamental requirements document and a necessary ingredient in achieving a successful BIM project. The provision on full and clear EIR is essential to achieving BIM Level-2. As Defined by PAS 1192-2, EIR is a “pre-tender document that sets out the information to be delivered and the standards and processes to be adopted by the supplier as part of the project delivery process”. It also notes that “EIR should be incorporated into tender documentation to enable suppliers to produce an initial BIM Execution Plan (BEP)”. The importance of effective definition of EIR lies in its contribution to a better productivity during the construction process in terms of cost and time, in addition to improving the quality of the built asset. Proper and clear information is a key aspect of the EIR, in terms of the information it contains and more importantly the information the client receives at the end of the project that will enable the effective management and operation of the asset, where typically about 60%-80% of the cost is spent. This paper reports on the research done in developing the Ontology-based tool for Employer Information Requirements (OntEIR). OntEIR has proven the ability to produce a full and complete set of EIRs, which ensures that the clients’ information needs for the final model delivered by BIM is clearly defined from the beginning of the process. It also reports on the work being done into transforming OntEIR into a smart tool for Defining Employer Information Requirements (smartEIR). smartEIR transforms the OntEIR tool into enabling it to develop custom EIR- tailored for the: Project Type, Project Requirements, and the Client Capabilities. The initial idea behind smartEIR is moving away from the notion “One EIR fits All”. smartEIR utilizes the links made in OntEIR and creating a 3D matrix that transforms it into a smart tool. The OntEIR tool is based on the OntEIR framework that utilizes both Ontology and the Decomposition of Goals to elicit and extract the complete set of requirements needed for a full and comprehensive EIR. A new ctaegorisation system for requirements is also introduced in the framework and tool, which facilitates the understanding and enhances the clarification of the requirements especially for novice clients. Findings of the evaluation of the tool that was done with experts in the industry, showed that the OntEIR tool contributes towards effective and efficient development of EIRs that provide a better understanding of the information requirements as requested by BIM, and support the production of a complete BIM Execution Plan (BEP) and a Master Information Delivery Plan (MIDP).

Keywords: building information modelling, employer information requirements, ontology, web-based, tool

Procedia PDF Downloads 123

Authors: Hemin Yuan, Yun Wang, Xiangchun Wang

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Gas hydrate is drawing attention due to the fact that it has an enormous amount all over the world, which is almost twice the conventional hydrocarbon reserves, making it a potential alternative source of energy. It is widely distributed in permafrost and continental ocean shelves, and many countries have launched national programs for investigating the gas hydrate. Gas hydrate is mainly explored through seismic methods, which include bottom simulating reflectors (BSR), amplitude blanking, and polarity reverse. These seismic methods are effective at finding the gas hydrate formations but usually contain large uncertainties when applying to invert the micro-scale petrophysical properties of the formations due to lack of constraints. Rock physics modeling links the micro-scale structures of the rocks to the macro-scale elastic properties and can work as effective constraints for the seismic methods. A number of rock physics models have been proposed for gas hydrate modeling, which addresses different mechanisms and applications. However, these models are generally not well classified, and it is confusing to determine the appropriate model for a specific study. Moreover, since the modeling usually involves multiple models and steps, it is difficult to determine the source of uncertainties. To solve these problems, we summarize the developed models/methods and make four classifications of the models according to the hydrate micro-scale morphology in sediments, the purpose of reservoir characterization, the stage of gas hydrate generation, and the lithology type of hosting sediments. Some sub-categories may overlap each other, but they have different priorities. Besides, we also analyze the priorities of different models, bring up the shortcomings, and explain the appropriate application scenarios. Moreover, by comparing the models, we summarize a general workflow of the modeling procedure, which includes rock matrix forming, dry rock frame generating, pore fluids mixing, and final fluid substitution in the rock frame. These procedures have been widely used in various gas hydrate modeling and have been confirmed to be effective. We also analyze the potential sources of uncertainties in each modeling step, which enables us to clearly recognize the potential uncertainties in the modeling. In the end, we explicate the general problems of the current models, including the influences of pressure and temperature, pore geometry, hydrate morphology, and rock structure change during gas hydrate dissociation and re-generation. We also point out that attenuation is also severely affected by gas hydrate in sediments and may work as an indicator to map gas hydrate concentration. Our work classifies rock physics models of gas hydrate into different categories, generalizes the modeling workflow, analyzes the modeling uncertainties and potential problems, which can facilitate the rock physics characterization of gas hydrate bearding sediments and provide hints for future studies.

Keywords: gas hydrate, rock physics model, modeling classification, hydrate morphology

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Authors: Biki Sarmah, Priyanko Raj Mudiar

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In the modern world of remotely interactive virtual reality-based learning and teaching, including professional skill-building training and acquisition practices, as well as data acquisition and robotic systems, the revolutionary application or implementation of field-programmable neurostimulator aids and first-hand interactive sensitisation techniques into 3D holographic audio-visual platforms have been a coveted dream of many scholars, professionals, scientists, and students. Integration of 'kinaesthetic vibrotactile haptic perception' along with an actuated step magnitude contact profiloscopy in augmented reality-based learning platforms and professional training can be implemented by using an extremely calculated and well-coordinated image telemetry including remote data mining and control technique. A real-time, computer-aided (PLC-SCADA) field calibration based algorithm must be designed for the purpose. But most importantly, in order to actually realise, as well as to 'interact' with some 3D holographic models displayed over a remote screen using remote laser image telemetry and control, all spatio-physical parameters like cardinal alignment, gyroscopic compensation, as well as surface profile and thermal compositions, must be implemented using zero-order type 1 actuators (or transducers) because they provide zero hystereses, zero backlashes, low deadtime as well as providing a linear, absolutely controllable, intrinsically observable and smooth performance with the least amount of error compensation while ensuring the best ergonomic comfort ever possible for the users.

Keywords: haptic feedback, kinaesthetic vibrotactile 3D design, medical simulation training, piezo diaphragm based actuator

Procedia PDF Downloads 161

Authors: Laura Canagueral-Pellice, Antonio Munar-Frau, Adaia Valls-Ontanon, Joao Carames, Federico Hernandez-Alfaro, Jordi Caballe-Serrano

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Objective: Guided bone regeneration (GBR) aims to replace the missing bone with a new structure to achieve long-term stability of rehabilitations. The aim of the present systematic review and meta-analysis is to determine the effect of barrier membranes on histological outcomes after GBR procedures. Moreover, the effect of the grafting material and tissue gain were analyzed. Materials & methods: Two independent reviewers performed an electronic search in Pubmed and Scopus, identifying all eligible publications up to March 2020. Only randomized controlled trials (RCTs) assessing a histological analysis of augmented areas were included. Results: A total of 6 publications were included for the present systematic review. A total of 110 biopsied sites were analysed; 10 corresponded to vertical bone augmentation procedures, whereas 100 analysed horizontal regeneration procedures. A mean tissue gain of 3 ± 1.48mm was obtained for horizontal defects. Histological assessment of new bone formation, residual particle and sub-epithelial connective tissue (SCT) was reported. The four main barrier membranes used were natural collagen membranes, e-PTFE, polylactic resorbable membranes and acellular dermal matrix membranes (AMDG). The analysis demonstrated that resorbable membranes result in higher values of new bone formation and lower values of residual particles and SCT. Xenograft resulted in lower new bone formation compared to allograft; however, no statistically significant differences were observed regarding residual particle and SCT. Overall, regeneration procedures adding autogenous bone, plasma derivate or growth factors achieved in general greater new bone formation and tissue gain. Conclusions: There is limited evidence favoring the effect of a certain type of barrier membrane in GBR. Data needs to be evaluated carefully; however, resorbable membranes are correlated with greater new bone formation values, especially when combined with allograft materials and/or the addition of autogenous bone, platelet reach plasma (PRP) or growth factors in the regeneration area. More studies assessing the histological outcomes of different GBR protocols and procedures testing different biomaterials are needed to maximize the clinical and histological outcomes in bone regeneration science.

Keywords: barrier membrane, graft material, guided bone regeneration, implant surgery, histology

Procedia PDF Downloads 148

Authors: Levent Basayigit, Sinan Demir, Burhan Kara, Yusuf Ucar

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Cannabis is mostly used for drug production. In some countries, an excessive amount of illegal cannabis is cultivated and sold. Most of the illegal cannabis cultivation occurs on the lands far from settlements. In farmlands, it is cultivated with other crops. In this method, cannabis is surrounded by tall plants like corn and sunflower. It is also cultivated with tall crops as the mixed culture. The common method of the determination of the illegal cultivation areas is to investigate the information obtained from people. This method is not sufficient for the determination of illegal cultivation in remote areas. For this reason, more effective methods are needed for the determination of illegal cultivation. Remote Sensing is one of the most important technologies to monitor the plant growth on the land. The aim of this study is to monitor cannabis cultivation area using satellite imagery. The main purpose of this study was to develop an applicable method for monitoring the cannabis cultivation. For this purpose, cannabis was grown as single or surrounded by the corn and sunflower in plots. The morphological characteristics of cannabis were recorded two times per month during the vegetation period. The spectral signature library was created with the spectroradiometer. The parcels were monitored with high-resolution satellite imagery. With the processing of satellite imagery, the cultivation areas of cannabis were classified. To separate the Cannabis plots from the other plants, the multiresolution segmentation algorithm was found to be the most successful for classification. WorldView Improved Vegetative Index (WV-VI) classification was the most accurate method for monitoring the plant density. As a result, an object-based classification method and vegetation indices were sufficient for monitoring the cannabis cultivation in multi-temporal Earthwiev images.

Keywords: Cannabis, drug, remote sensing, object-based classification

Procedia PDF Downloads 267

Authors: G. S. Ayyappan, Srinivas Kota, Jaffer R. C. Sheriff, C. Prakash Chandra Joshua

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In the present scenario of energy crises, energy conservation in the electrical machines is very important in the industries. In order to conserve energy, one needs to monitor the performance of an induction motor on-site and in-situ. The instruments available for this purpose are very meager and very expensive. This paper deals with the design and development of induction motor performance analyser on-line, on-site, and in-situ. The system measures only few electrical input parameters like input voltage, line current, power factor, frequency, powers, and motor shaft speed. These measured data are coupled to name plate details and compute the operating efficiency of induction motor. This system employs the method of computing motor losses with the help of equivalent circuit parameters. The equivalent circuit parameters of the concerned motor are estimated using the developed algorithm at any load conditions and stored in the system memory. The developed instrument is a reliable, accurate, compact, rugged, and cost-effective one. This portable instrument could be used as a handy tool to study the performance of both slip ring and cage induction motors. During the analysis, the data can be stored in SD Memory card and one can perform various analyses like load vs. efficiency, torque vs. speed characteristics, etc. With the help of the developed instrument, one can operate the motor around its Best Operating Point (BOP). Continuous monitoring of the motor efficiency could lead to Life Cycle Assessment (LCA) of motors. LCA helps in taking decisions on motor replacement or retaining or refurbishment.

Keywords: energy conservation, equivalent circuit parameters, induction motor efficiency, life cycle assessment, motor performance analysis

Procedia PDF Downloads 375

Authors: Yuekun Chen, Yousef Sardahi, Salam Hajjar, Christopher Greer

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This paper presents a multi-objective optimal design of a cascade control system for an underactuated mechanical system. Cascade control structures usually include two control algorithms (inner and outer). To design such a control system properly, the following conflicting objectives should be considered at the same time: 1) the inner closed-loop control must be faster than the outer one, 2) the inner loop should fast reject any disturbance and prevent it from propagating to the outer loop, 3) the controlled system should be insensitive to measurement noise, and 4) the controlled system should be driven by optimal energy. Such a control problem can be formulated as a multi-objective optimization problem such that the optimal trade-offs among these design goals are found. To authors best knowledge, such a problem has not been studied in multi-objective settings so far. In this work, an underactuated mechanical system consisting of a rotary servo motor and a ball and beam is used for the computer simulations, the setup parameters of the inner and outer control systems are tuned by NSGA-II (Non-dominated Sorting Genetic Algorithm), and the dominancy concept is used to find the optimal design points. The solution of this problem is not a single optimal cascade control, but rather a set of optimal cascade controllers (called Pareto set) which represent the optimal trade-offs among the selected design criteria. The function evaluation of the Pareto set is called the Pareto front. The solution set is introduced to the decision-maker who can choose any point to implement. The simulation results in terms of Pareto front and time responses to external signals show the competing nature among the design objectives. The presented study may become the basis for multi-objective optimal design of multi-loop control systems.

Keywords: cascade control, multi-Loop control systems, multiobjective optimization, optimal control

Procedia PDF Downloads 148

Authors: Chen Jun-Hong, He Pu, Tao Wen-Quan

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Proton exchange membrane fuel cell (PEMFC) is the most promising future energy source owing to its low operating temperature, high energy efficiency, high power density, and environmental friendliness. In this paper, a comprehensive PEMFC system control-oriented model is developed in the Matlab/Simulink environment, which includes the hydrogen supply subsystem, air supply subsystem, and thermal management subsystem. Besides, Improved Artificial Bee Colony (IABC) is used in the parameter identification of PEMFC semi-empirical equations, making the maximum relative error between simulation data and the experimental data less than 0.4%. Operation temperature is essential for PEMFC, both high and low temperatures are disadvantageous. In the thermal management subsystem, water pump and fan are both controlled with the PID controller to maintain the appreciate operation temperature of PEMFC for the requirements of safe and efficient operation. To improve the control effect further, fuzzy control is introduced to optimize the PID controller of the pump, and the Radial Basis Function (RBF) neural network is introduced to optimize the PID controller of the fan. The results demonstrate that Fuzzy-PID and RBF-PID can achieve a better control effect with 22.66% decrease in Integral Absolute Error Criterion (IAE) of T_st (Temperature of PEMFC) and 77.56% decrease in IAE of T_in (Temperature of inlet cooling water) compared with traditional PID. In the end, a novel thermal management structure is proposed, which uses the cooling air passing through the main radiator to continue cooling the secondary radiator. In this thermal management structure, the parasitic power dissipation can be reduced by 69.94%, and the control effect can be improved with a 52.88% decrease in IAE of T_in under the same controller.

Keywords: PEMFC system, parameter identification, temperature control, Fuzzy-PID, RBF-PID, parasitic power

Procedia PDF Downloads 80

Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

Procedia PDF Downloads 102

Authors: Raj Kumar, Prem Felix Siril

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The poor aqueous solubility of many pharmaceutical drugs and potential drug candidates is a big challenge in drug development. Nanoformulation of such candidates is one of the major solutions for the delivery of such drugs. We initially developed the evaporation assisted solvent-antisolvent interaction (EASAI) method. EASAI method is use full to prepared nanoparticles of poor water soluble drugs with spherical morphology and particles size below 100 nm. However, to further improve the effect formulation to reduce number of dose and side effect it is important to control the delivery of drugs. However, many drug delivery systems are available. Among the many nano-drug carrier systems, solid lipid nanoparticles (SLNs) have many advantages over the others such as high biocompatibility, stability, non-toxicity and ability to achieve controlled release of drugs and drug targeting. SLNs can be administered through all existing routes due to high biocompatibility of lipids. SLNs are usually composed of lipid, surfactant and drug were encapsulated in lipid matrix. A number of non-steroidal anti-inflammatory drugs (NSAIDs) have poor bioavailability resulting from their poor aqueous solubility. In the present work, SLNs loaded with NSAIDs such as Nabumetone (NBT), Ketoprofen (KP) and Ibuprofen (IBP) were successfully prepared using different lipids and surfactants. We studied and optimized experimental parameters using a number of lipids, surfactants and NSAIDs. The effect of different experimental parameters such as lipid to surfactant ratio, volume of water, temperature, drug concentration and sonication time on the particles size of SLNs during the preparation using hot-melt sonication was studied. It was found that particles size was directly proportional to drug concentration and inversely proportional to surfactant concentration, volume of water added and temperature of water. SLNs prepared at optimized condition were characterized thoroughly by using different techniques such as dynamic light scattering (DLS), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), X-ray diffraction (XRD) and differential scanning calorimetry and Fourier transform infrared spectroscopy (FTIR). We successfully prepared the SLN of below 220 nm using different lipids and surfactants combination. The drugs KP, NBT and IBP showed 74%, 69% and 53% percentage of entrapment efficiency with drug loading of 2%, 7% and 6% respectively in SLNs of Campul GMS 50K and Gelucire 50/13. In-vitro drug release profile of drug loaded SLNs is shown that nearly 100% of drug was release in 6 h.

Keywords: nanoparticles, delivery, solid lipid nanoparticles, hot-melt sonication, poor water soluble drugs, solubility, bioavailability

Procedia PDF Downloads 310

Authors: Luay Jum'a

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Many challenges are facing third-party logistics (3PL) providers in the domestic and global markets which create a volatile decision making environment. All these challenges such as managing changes in consumer behaviour, demanding expectations from customers and time compressions have turned into complex problems for 3PL providers. Since the movement towards increased outsourcing outpaces movement towards insourcing, the need to achieve a competitive advantage over competitors in 3PL market increases. This trend continues to grow over the years and as a result, areas of strengths and improvements are highlighted through the analysis of the LSQ factors that lead to B2B customers’ satisfaction which become a priority for 3PL companies. Consequently, 3PL companies are increasingly focusing on the most important issues from the perspective of their customers and relying more on this value of information in making their managerial decisions. Therefore, this study is concerned with providing guidance for improving logistics service quality (LSQ) levels in the context of 3PL industry in Jordan. The study focused on the most important factors in LSQ and used a managerial tool that guides 3PL companies in making LSQ improvements based on a quadrant analysis of two main dimensions: LSQ declared importance and LSQ inferred importance. Although, a considerable amount of research has been conducted to investigate the relationship between logistics service quality (LSQ) and customer satisfaction, there remains a lack of developing managerial tools to aid in the process of LSQ improvement decision-making. Moreover, the main advantage for the companies to use 3PL service providers as a trend is due to the realised percentage of cost reduction on the total cost of logistics operations and the incremental improvement in customer service. In this regard, having a managerial tool that help 3PL service providers in managing the LSQ factors portfolio effectively and efficiently would be a great investment for service providers. One way of suggesting LSQ improvement actions for 3PL service providers is via the adoption of analysis tools that perform attribute categorisation such as Importance–Performance matrix. In mind of the above, it can be stated that the use of quadrant analysis will provide a valuable opportunity for 3PL service providers to identify improvement opportunities as customer service attributes or factors importance are identified in two different techniques that complete each other. Moreover, the data were collected through conducting a survey and 293 questionnaires were returned from business-to-business (B2B) customers of 3PL companies in Jordan. The results showed that the LSQ factors vary in their importance and 3PL companies should focus on some LSQ factors more than other factors. Moreover, ordering procedures, timeliness/responsiveness LSQ factors considered being crucial in 3PL businesses and therefore they need to have more focus and development by 3PL service providers in the Jordanian market.

Keywords: logistics service quality, managerial decisions, quadrant analysis, third party logistics service provider

Procedia PDF Downloads 124

Authors: Henrry Vicente Rojas Asuero, Holger Manuel Benavides Muñoz

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Approximately 80% of the energy consumed worldwide is generated from fossil sources, which are responsible for the emission of a large volume of greenhouse gases. For this reason, the global trend, at present, is the widespread use of energy produced from renewable sources. This seeks safety and diversification of energy supply, based on social cohesion, economic feasibility and environmental protection. In this scenario, small hydropower systems (P ≤ 10MW) stand out due to their high efficiency, economic competitiveness and low environmental impact. Small hydropower systems, along with wind and solar energy, are expected to represent a significant percentage of the world's energy matrix in the near term. Among the various technologies present in the state of the art, relating to small hydropower systems, is the Gravitational Water Vortex Power Plant, a recent technology that excels because of its versatility of operation, since it can operate with jumps in the range of 0.70 m-2.00 m and flow rates from 1 m3/s to 20 m3/s. Its operating system is based on the utilization of the energy of rotation contained within a large water vortex artificially induced. This paper presents the study and experimental design of an optimal hydraulic structure with the capacity to induce the artificial formation of a gravitational water vortex trough a system of easy application and high efficiency, able to operate in conditions of very low head and minimum flow. The proposed structure consists of a channel, with variable base, vortex inductor, tangential flow generator, coupled to a circular tank with a conical transition bottom hole. In the laboratory test, the angular velocity of the water vortex was related to the geometric characteristics of the inductor channel, as well as the influence of the conical transition bottom hole on the physical characteristics of the water vortex. The results show angular velocity values of greater magnitude as a function of depth, in addition the presence of the conical transition in the bottom hole of the circular tank improves the water vortex formation conditions while increasing the angular velocity values. Thus, the proposed system is a sustainable solution for the energy supply of rural areas near to watercourses.

Keywords: experimental model, gravitational water vortex power plant, renewable energy, small hydropower

Procedia PDF Downloads 285

Authors: Nico Rosamilia

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The study evaluates the importance of non-financial disclosure practices for regulators, investors, businesses, and markets. It aims to create a sector-specific set of indicators for environmental, social, and governance (ESG) performances alternative to the ratings of the agencies. The existing literature extensively studies the implementation of ESG rating systems. Conversely, this study has a twofold outcome. Firstly, it should generalize incentive systems and governance policies for ESG and sustainable principles. Therefore, it should contribute to the EU Sustainable Finance Disclosure Regulation. Secondly, it concerns the market and the investors by highlighting successful sustainable investing. Indeed, the study contemplates the effect of ESG adoption practices on corporate value. The research explores the asset pricing angle in order to shed light on the fragmented argument on the finance of ESG. Investors may be misguided about the positive or negative effects of ESG on performances. The paper proposes a different method to evaluate ESG performances. By comparing the results of a traditional econometric approach (Lasso) with a machine learning algorithm (Random Forest), the study establishes a set of indicators for ESG performance. Therefore, the research also empirically contributes to the theoretical strands of literature regarding model selection and variable importance in a finance framework. The algorithms will spit out sector-specific indicators. This set of indicators defines an alternative to the compounded scores of ESG rating agencies and avoids the possible offsetting effect of scores. With this approach, the paper defines a sector-specific set of indicators to standardize ESG disclosure. Additionally, it tries to shed light on the absence of a clear understanding of the direction of the ESG effect on corporate value (the problem of endogeneity).

Keywords: ESG ratings, non-financial information, value of firms, sustainable finance

Procedia PDF Downloads 79

Authors: Adesuyi Ayodeji Steve, Zahn Munch

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This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

Procedia PDF Downloads 395

Authors: Kaushik Vaideeswaran, Jean Gobet, Patrick Margraf, Olha Sereda

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Nitrocarburisation is a surface hardening technique often applied to improve the wear resistance of steel surfaces. It is considered to be a promising solution in comparison with other processes such as flame spraying, owing to the formation of a diffusion layer which provides mechanical integrity, as well as its cost-effectiveness. To improve other tribological properties of the surface such as the coefficient of friction (COF), dry lubricants are utilized. Currently, the lifetime of steel components in many applications using either of these techniques individually are faced with the limitations of the two: high COF for nitrocarburized surfaces and low wear resistance of dry lubricant coatings. To this end, the current study involves the creation of a hybrid surface using the impregnation of a dry lubricant on to a nitrocarburized surface. The mechanical strength and hardness of Gerster SA’s nitrocarburized surfaces accompanied by the impregnation of the porous outermost layer with a solid lubricant will create a hybrid surface possessing both outstanding wear resistance and a low friction coefficient and with high adherence to the substrate. Gerster SA has the state-of-the-art technology for the surface hardening of various steels. Through their expertise in the field, the nitrocarburizing process parameters (atmosphere, temperature, dwelling time) were optimized to obtain samples that have a distinct porous structure (in terms of size, shape, and density) as observed by metallographic and microscopic analyses. The porosity thus obtained is suitable for the impregnation of a dry lubricant. A commercially available dry lubricant with a thermoplastic matrix was employed for the impregnation process, which was optimized to obtain a void-free interface with the surface of the nitrocarburized layer (henceforth called hybrid surface). In parallel, metallic samples without nitrocarburisation were also impregnated with the same dry lubricant as a reference (henceforth called reference surface). The reference and the nitrocarburized surfaces, with and without the dry lubricant were tested for their tribological behavior by sliding against a quenched steel ball using a nanotribometer. Without any lubricant, the nitrocarburized surface showed a wear rate 5x lower than the reference metal. In the presence of a thin film of dry lubricant ( < 2 micrometers) and under the application of high loads (500 mN or ~800 MPa), while the COF for the reference surface increased from ~0.1 to > 0.3 within 120 m, the hybrid surface retained a COF < 0.2 for over 400m of sliding. In addition, while the steel ball sliding against the reference surface showed heavy wear, the corresponding ball sliding against the hybrid surface showed very limited wear. Observations of the sliding tracks in the hybrid surface using Electron Microscopy show the presence of the nitrocarburized nodules as well as the lubricant, whereas no traces of the lubricant were found in the sliding track on the reference surface. In this manner, the clear advantage of combining nitrocarburisation with the impregnation of a dry lubricant towards forming a hybrid surface has been demonstrated.

Keywords: dry lubrication, hybrid surfaces, improved wear resistance, nitrocarburisation, steels

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Authors: C. U. Om Kumar, S. Kishore, A. Geetha

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An Operating system (OS) is software that manages computer hardware and software resources by providing services to computer programs. One of the important user expectations of the operating system is to provide the practice of defending information from unauthorized access, disclosure, modification, inspection, recording or destruction. Operating system is always vulnerable to the attacks of malwares such as computer virus, worm, Trojan horse, backdoors, ransomware, spyware, adware, scareware and more. And so the anti-virus software were created for ensuring security against the prominent computer viruses by applying a dictionary based approach. The anti-virus programs are not always guaranteed to provide security against the new viruses proliferating every day. To clarify this issue and to secure the computer system, our proposed expert system concentrates on authorizing the processes as wanted and unwanted by the administrator for execution. The Expert system maintains a database which consists of hash code of the processes which are to be allowed. These hash codes are generated using MD5 message-digest algorithm which is a widely used cryptographic hash function. The administrator approves the wanted processes that are to be executed in the client in a Local Area Network by implementing Client-Server architecture and only the processes that match with the processes in the database table will be executed by which many malicious processes are restricted from infecting the operating system. The add-on advantage of this proposed Expert system is that it limits CPU usage and minimizes resource utilization. Thus data and information security is ensured by our system along with increased performance of the operating system.

Keywords: virus, worm, Trojan horse, back doors, Ransomware, Spyware, Adware, Scareware, sticky software, process table, MD5, CPU usage and resource utilization

Procedia PDF Downloads 426

Authors: Irina Khutsishvili

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The presented study develops a decision support methodology for multi-criteria group decision-making problem. The proposed methodology is based on the TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) approach in the hesitant fuzzy environment. The main idea of decision-making problem is a selection of one best alternative or several ranking alternatives among a set of feasible alternatives. Typically, the process of decision-making is based on an evaluation of certain criteria. In many MCDM problems (such as medical diagnosis, project management, business and financial management, etc.), the process of decision-making involves experts' assessments. These assessments frequently are expressed in fuzzy numbers, confidence intervals, intuitionistic fuzzy values, hesitant fuzzy elements and so on. However, a more realistic approach is using linguistic expert assessments (linguistic variables). In the proposed methodology both the values and weights of the criteria take the form of linguistic variables, given by all decision makers. Then, these assessments are expressed in triangular fuzzy numbers. Consequently, proposed approach is based on triangular hesitant fuzzy TOPSIS decision-making model. Following the TOPSIS algorithm, first, the fuzzy positive ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS) are defined. Then the ranking of alternatives is performed in accordance with the proximity of their distances to the both FPIS and FNIS. Based on proposed approach the software package has been developed, which was used to rank investment projects in the real investment decision-making problem. The application and testing of the software were carried out based on the data provided by the ‘Bank of Georgia’.

Keywords: fuzzy TOPSIS approach, investment project, linguistic variable, multi-criteria decision making, triangular hesitant fuzzy set

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Authors: Yujiang Wu

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As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

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Authors: Larry Turkish

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Introduction: The NBA is an example of mediocrity and this will be evident in the following paper. The study examines and evaluates the characteristics of the NBA champions. As divisions and playoff teams increase, there is an increase in the probability that the champion originates from the mediocre category. Since it’s inception in 1947, the league has been mediocre and continues to this day. Why does a professional league allow any team with a less than 50% winning percentage into the playoffs? As long as the finances flow into the league, owners will not change the current algorithm. The objective of this paper is to determine if the regular season has meaning in finding an NBA champion. Statistical Analysis: The data originates from the NBA website. The following variables are part of the statistical analysis: Rank, the rank of a team relative to other teams in the league based on the regular season win-loss record; Winning Percentage of a team based on the regular season; Divisions, the number of divisions within the league and Playoff Teams, the number of playoff teams relative to a particular season. The following statistical applications are applied to the data: Pearson Product-Moment Correlation, Analysis of Variance, Factor and Regression analysis. Conclusion: The results indicate that the divisional structure and number of playoff teams results in a negative effect on the winning percentage of playoff teams. It also prevents teams with higher winning percentages from accessing the playoffs. Recommendations: 1. Teams that have a winning percentage greater than 1 standard deviation from the mean from the regular season will have access to playoffs. (Eliminates mediocre teams.) 2. Eliminate Divisions (Eliminates weaker teams from access to playoffs.) 3. Eliminate Conferences (Eliminates weaker teams from access to the playoffs.) 4. Have a balanced regular season schedule, (Reduces the number of regular season games, creates equilibrium, reduces bias) that will reduce the need for load management.

Keywords: alignment, mediocrity, regression, z-score

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Authors: Osman Adiguzel

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Shape memory effect is a peculiar property exhibited by certain alloy systems and based on martensitic transformation, and shape memory properties are closely related to the microstructures of the material. Shape memory effect is linked with martensitic transformation, which is a solid state phase transformation and occurs with the cooperative movement of atoms by means of lattice invariant shears on cooling from high-temperature parent phase. Lattice twinning and detwinning can be considered as elementary processes activated during the transformation. Thermally induced martensite occurs as martensite variants, in self-accommodating manner and consists of lattice twins. Also, this martensite is called the twinned martensite or multivariant martensite. Deformation of shape memory alloys in martensitic state proceeds through a martensite variant reorientation. The martensite variants turn into the reoriented single variants with deformation, and the reorientation process has great importance for the shape memory behavior. Copper based alloys exhibit this property in metastable β- phase region, which has DO3 –type ordered lattice in ternary case at high temperature, and these structures martensiticaly turn into the layered complex structures with lattice twinning mechanism, on cooling from high temperature parent phase region. The twinning occurs as martensite variants with lattice invariant shears in two opposite directions, <110 > -type directions on the {110}- type plane of austenite matrix. Lattice invariant shear is not uniform in copper based ternary alloys and gives rise to the formation of unusual layered structures, like 3R, 9R, or 18R depending on the stacking sequences on the close-packed planes of the ordered lattice. The unit cell and periodicity are completed through 18 atomic layers in case of 18R-structure. On the other hand, the deformed material recovers the original shape on heating above the austenite finish temperature. Meanwhile, the material returns to the twinned martensite structures (thermally induced martensite structure) in one way (irreversible) shape memory effect on cooling below the martensite finish temperature, whereas the material returns to the detwinned martensite structure (deformed martensite) in two-way (reversible) shape memory effect. Shortly one can say that the microstructural mechanisms, responsible for the shape memory effect are the twinning and detwinning processes as well as martensitic transformation. In the present contribution, x-ray diffraction, transmission electron microscopy (TEM) and differential scanning calorimetry (DSC) studies were carried out on two copper-based ternary alloys, CuZnAl, and CuAlMn.

Keywords: shape memory effect, martensitic transformation, twinning and detwinning, layered structures

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Authors: Antonieta Heyden Megale

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With the advent of globalization, the social, cultural and linguistic situation of the whole world has changed. In this scenario, the teaching of English, in Brazil, has become a booming business and the belief that this language is essential to a successful life is played by the media that sees it as a commodity and spares no effort to sell it. In this context, it has become evident the growth of bilingual and international schools that have English and Portuguese as languages of instruction. According to federal legislation, all schools in the country must follow the Curriculum guidelines proposed by the Ministry of Education of Brazil. It is then mandatory that, in addition to the specific foreign curriculum an international school subscribes to, it must also teach all subjects of the official minimum curriculum and these subjects have to be taught in Portuguese. It is important to emphasize that, in these schools, English is the most prestigious language. Therefore, firstly, Brazilian teachers who teach Portuguese in such contexts find themselves in a situation in which they teach in a low-status language. Secondly, because such teachers’ actions are guided by a different cultural matrix, which differs considerably from Anglo-Saxon values and beliefs, they often experience intercultural conflict in their workplace. Taking it consideration, this research, focusing on the trajectories of a specific group of Brazilian teachers of Portuguese in international and bilingual schools located in the city of São Paulo, intends to analyze how they discursively represent their own professional identities and practices. More specifically the objectives of this research are to understand, from the perspective of the investigated teachers, how they (i) rebuilt narratively their professional careers and explain the factors that led them to an international or to an immersion bilingual school; (ii) position themselves with respect to their linguistic repertoire; (iii) interpret the intercultural practices they are involved with in school and (v) position themselves by foregrounding categories to determine their membership in the group of Portuguese teachers. We have worked with these teachers’ autobiographical narratives. The autobiographical approach assumes that the stories told by teachers are systems of meaning involved in the production of identities and subjectivities in the context of power relations. The teachers' narratives were elicited by the following trigger: "I would like you to tell me how you became a teacher in a bilingual/international school and what your impressions are about your work and about the context in which it is inserted". These narratives were produced orally, recorded, and transcribed for analysis. The teachers were also invited to draw their "linguistic portraits". The theoretical concepts of positioning and the indexical cues were taken into consideration in data analysis. The narratives produced by the teachers point to intercultural conflicts related to their expectations and representations of others, which are never neutral or objective truths but discursive constructions.

Keywords: bilingual schools, identity, interculturality, narrative

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Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: Ramkumar R., Gaurav Shinde, Pratik Shroff, Sachin Kumar Jain, Nagesh Walke

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Advancements in electric vehicles have significantly reduced the powertrain noise and moving components of vehicles. As a result, in-cab noises have become more noticeable to passengers inside the car. To ensure a comfortable ride for drivers and other passengers, it has become crucial to eliminate undesirable component noises during the development phase. Standard practices are followed to identify the severity of noises based on subjective ratings, but it can be a tedious process to identify the severity of each development sample and make changes to reduce it. Additionally, the severity rating can vary from jury to jury, making it challenging to arrive at a definitive conclusion. To address this, an automotive component was identified to evaluate squeak and rattle noise issue. Physical tests were carried out for random and sine excitation profiles. Aim was to subjectively assess the noise using jury rating method and objectively evaluate the same by measuring the noise. Suitable jury evaluation method was selected for the said activity, and recorded sounds were replayed for jury rating. Objective data sound quality metrics viz., loudness, sharpness, roughness, fluctuation strength and overall Sound Pressure Level (SPL) were measured. Based on this, correlation co-efficients was established to identify the most relevant sound quality metrics that are contributing to particular identified noise issue. Regression analysis was then performed to establish the correlation between subjective and objective data. Mathematical model was prepared using artificial intelligence and machine learning algorithm. The developed model was able to predict the subjective rating with good accuracy.

Keywords: BSR, noise, correlation, regression

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Authors: Silvia Portarena, Chiara Anselmi, Chiara Baldacchini, Enrico Brugnoli

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Extra virgin olive oil (EVOO) is a complex matrix mainly composed by fatty acid and other minor compounds, among which carotenoids are well known for their antioxidative function that is a key mechanism of protection against cancer, cardiovascular diseases, and macular degeneration in humans. EVOO composition in terms of such constituents is generally the result of a complex combination of genetic, agronomical and environmental factors. To selectively improve the quality of EVOOs, the role of each factor on its biochemical composition need to be investigated. By selecting fruits from four different cultivars similarly grown and harvested, it was demonstrated that Raman spectroscopy, combined with chemometric analysis, is able to discriminate the different cultivars, also as a function of the harvest date, based on the relative content and composition of fatty acid and carotenoids. In particular, a correct classification up to 94.4% of samples, according to the cultivar and the maturation stage, was obtained. Moreover, by using gas chromatography and high-performance liquid chromatography as reference techniques, the Raman spectral features further allowed to build models, based on partial least squares regression, that were able to predict the relative amount of the main fatty acids and the main carotenoids in EVOO, with high coefficients of determination. Besides genetic factors, climatic parameters, such as light exposition, distance from the sea, temperature, and amount of precipitations could have a strong influence on EVOO composition of both major and minor compounds. This suggests that the Raman spectra could act as a specific fingerprint for the geographical discrimination and authentication of EVOO. To understand the influence of environment on EVOO Raman spectra, samples from seven regions along the Italian coasts were selected and analyzed. In particular, it was used a dual approach combining Raman spectroscopy and isotope ratio mass spectrometry (IRMS) with principal component and linear discriminant analysis. A correct classification of 82% EVOO based on their regional geographical origin was obtained. Raman spectra were obtained by Super Labram spectrometer equipped with an Argon laser (514.5 nm wavelenght). Analyses of stable isotope content ratio were performed using an isotope ratio mass spectrometer connected to an elemental analyzer and to a pyrolysis system. These studies demonstrate that RR spectroscopy is a valuable and useful technique for the analysis of EVOO. In combination with statistical analysis, it makes possible the assessment of specific samples’ content and allows for classifying oils according to their geographical and varietal origin.

Keywords: authentication, chemometrics, olive oil, raman spectroscopy

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Authors: Ebrahim Ghasemi Ardi, Ai Bing Yu, Run Yu Yang

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Current study is aimed to develop an available in-house discrete element method (DEM) code and link it with direct breakage event. So, it became possible to determine the particle breakage and then its fragments size distribution, simultaneous with DEM simulation. It directly applies the particle breakage inside the DEM computation algorithm and if any breakage happens the original particle is replaced with daughters. In this way, the calculation will be followed based on a new updated particles list which is very similar to the real grinding environment. To validate developed model, a grinding ball impacting an unconfined particle bed was simulated. Since considering an entire ball mill would be too computationally demanding, this method provided a simplified environment to test the model. Accordingly, a representative volume of the ball mill was simulated inside a box, which could emulate media (ball)–powder bed impacts in a ball mill and during particle bed impact tests. Mono, binary and ternary particle beds were simulated to determine the effects of granular composition on breakage kinetics. The results obtained from the DEM simulations showed a reduction in the specific breakage rate for coarse particles in binary mixtures. The origin of this phenomenon, commonly known as cushioning or decelerated breakage in dry milling processes, was explained by the DEM simulations. Fine particles in a particle bed increase mechanical energy loss, and reduce and distribute interparticle forces thereby inhibiting the breakage of the coarse component. On the other hand, the specific breakage rate of fine particles increased due to contacts associated with coarse particles. Such phenomenon, known as acceleration, was shown to be less significant, but should be considered in future attempts to accurately quantify non-linear breakage kinetics in the modeling of dry milling processes.

Keywords: particle bed, breakage models, breakage kinetic, discrete element method

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Authors: Akintunde Alo

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The incessant encroachments into the forest ecosystem by the farmers and local contractors constitute a major threat to the conservation of genetic resources and biodiversity in Nigeria. To propose a viable monitoring system, this study employed Geographic Information System (GIS) technology to assess the changes that occurred for a period of five years (between 2011 and 2016) in Ikere forest reserve. Landsat imagery of the forest reserve was obtained. For the purpose of geo-referencing the acquired satellite imagery, ground-truth coordinates of some benchmark places within the forest reserve was relied on. Supervised classification algorithm, image processing, vectorization and map production were realized using ArcGIS. Various land use systems within the forest ecosystem were digitized into polygons of different types and colours for 2011 and 2016, roads were represented with lines of different thickness and colours. Of the six land-use delineated, the grassland increased from 26.50 % in 2011 to 45.53% in 2016 of the total land area with a percentage change of 71.81 %. Plantations of Gmelina arborea and Tectona grandis on the other hand reduced from 62.16 % in 2011 to 27.41% in 2016. The farmland and degraded land recorded percentage change of about 176.80 % and 8.70 % respectively from 2011 to 2016. Overall, the rate of deforestation in the study area is on the increase and becoming severe. About 72.59% of the total land area has been converted to non-forestry uses while the remnant 27.41% is occupied by plantations of Gmelina arborea and Tectona grandis. Interestingly, over 55 % of the plantation area in 2011 has changed to grassland, or converted to farmland and degraded land in 2016. The rate of change over time was about 9.79 % annually. Based on the results, rapid actions to prevail on the encroachers to stop deforestation and encouraged re-afforestation in the study area are recommended.

Keywords: land use change, forest reserve, satellite imagery, geographical information system

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Authors: Mahesh Kumar Jat, Manisha Choudhary

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Rapid population and economic growth resulted in changes in large-scale land use land cover (LULC) changes. Changes in the biophysical properties of the Earth's surface and its impact on climate are of primary concern nowadays. Different approaches, ranging from location-based relationships or modelling earth surface - atmospheric interaction through modelling techniques like surface energy balance (SEB) have been used in the recent past to examine the relationship between changes in Earth surface land cover and climatic characteristics like temperature and precipitation. A remote sensing-based model i.e., Surface Energy Balance Algorithm for Land (SEBAL), has been used to estimate the surface heat fluxes over Mahi Bajaj Sagar catchment (India) from 2001 to 2020. Landsat ETM and OLI satellite data are used to model the SEB of the area. Changes in observed precipitation and temperature, obtained from India Meteorological Department (IMD) have been correlated with changes in surface heat fluxes to understand the relative contributions of LULC change in changing these climatic variables. Results indicate a noticeable impact of LULC changes on climatic variables, which are aligned with respective changes in SEB components. Results suggest that precipitation increases at a rate of 20 mm/year. The maximum and minimum temperature decreases and increases at 0.007 ℃ /year and 0.02 ℃ /year, respectively. The average temperature increases at 0.009 ℃ /year. Changes in latent heat flux and sensible heat flux positively correlate with precipitation and temperature, respectively. Variation in surface heat fluxes influences the climate parameters and is an adequate reason for climate change. So, SEB modelling is helpful to understand the LULC change and its impact on climate.

Keywords: remote sensing, GIS, object based, classification

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Authors: Milan Joshi, Harsh Agrawal, Pallaw Mishra, Sanand Sule

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Predicting electricity usage is a crucial aspect of organizing and controlling sustainable energy systems. The task of forecasting electricity load is intricate and requires a lot of effort due to the combined impact of social, economic, technical, environmental, and cultural factors on power consumption in communities. As a result, it is important to create strong models that can handle the significant non-linear and complex nature of the task. The objective of this study is to create and compare three machine learning techniques for predicting electricity load for both the day ahead and intraday, taking into account various factors such as meteorological data and social events including holidays and festivals. The proposed methods include a LightGBM, FBProphet, combination of FBProphet and LightGBM for day ahead and Motifs( Stumpy) based on Mueens algorithm for similarity search for intraday. We utilize these techniques to predict electricity usage during normal days and social events in the Himachal Region. We then assess their performance by measuring the MSE, RMSE, and MAPE values. The outcomes demonstrate that the combination of FBProphet and LightGBM method is the most accurate for day ahead and Motifs for intraday forecasting of electricity usage, surpassing other models in terms of MAPE, RMSE, and MSE. Moreover, the FBProphet - LightGBM approach proves to be highly effective in forecasting electricity load during social events, exhibiting precise day ahead predictions. In summary, our proposed electricity forecasting techniques display excellent performance in predicting electricity usage during normal days and special events in the Himachal Region.

Keywords: feature engineering, FBProphet, LightGBM, MASS, Motifs, MAPE

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Authors: Ilme Faridatul, Bo Wu

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Bangladesh is often called as a motherland of rivers. It contains about 700 rivers among all these the Padma River is one of the largest rivers of Bangladesh. The change of river bank and erosion has become a common environmental natural hazard in Bangladesh. The river banks are under intense pressure from natural processes such as erosion and accretion as well as anthropogenic processes such as urban growth and pollution. The Padma River is flowing along ten districts of Bangladesh among all these Faridpur district is most vulnerable to river bank erosion. The severity of the river erosion is so high that each year a thousand of populations become homeless and lose their agricultural lands. Though the Faridpur district is most vulnerable to river bank erosion no specific research has been conducted to identify the changing pattern of river bank along this district. The outcome of the research may serve as guidance to prepare river bank monitoring program and management. This research has utilized integrated techniques of remote sensing and geographic information system to monitor the changes from 1995 to 2015 at Faridpur district. To discriminate the land water interface Modified Normalized Difference Water Index (MNDWI) algorithm is applied and on screen digitization approach is used over MNDWI images of 1995, 2002 and 2015 for river bank line extraction. The extent of changes in the river bank along Faridpur district is estimated through overlaying the digitized maps of all three years. The river bank lines are highlighted to infer the erosion and accretion and the changes are calculated. The result shows that the middle of the river is gaining land through sedimentation and the both side river bank is shifting causing severe erosion that consequently resulting the loss of farmland and homestead. Over the study period from 1995 to 2015 it witnessed huge erosion and accretion that played an active role in the changes of the river bank.

Keywords: river bank, erosion and accretion, change monitoring, remote sensing

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