Artificial Intelligence & Neural Networking

An overview of Hydropower application to the waste water (Sewage) is described here. Firstly the waste water treated in Sewage Treatment plant. It includes physical, chemical and biological contaminants. Its objective is to produce an environmentally. Here the solid waste (treated sludge) suitable for disposal of reuse (usually for fertilizers) and treated water used for generating electricity by a small Hydro power plant. Treated water of sewage at a high pressure or flowing with a high velocity can be used to run turbine or water wheel coupled to Generator and therefore of electrical power is becoming more and more popular as it is reliable and requires least maintenance and care. Output power has been estimated for available different head and flow rate of the waste water. Various types of the turbine-generator sets for different available head have also suggested for reliable operation of the developed this plant. The main advantage of this power plant is an independent power pl...

Today, the amount of data obtained from onboard submarine systems and external sources is beyond human perception and cognitive abilities. The operational environment's complexity has increased personnel's physical and mental workload; however, personnel recruitment constraints have made it necessary to perform tasks with a limited workforce. Hence, a Command and Control Information Systems (C2IS), supported by data-driven decision-making, is necessary for all functional areas on board the submarine to meet operational needs and take advantage of technology. Such a C2IS is required by the upcoming "Multi-Domain Operations, MDO" and "Cross-Domain Command, CDC" concepts. In this study, in order to define the need and to identify potential areas for solutions, the historical background of submarine CMSs operational requirements have been analyzed and classified, requirement areas have been determined, fundamental approaches toward solutions have been defined, and a conceptual framework has been established. As a result of the studies, a data-driven "United Command and Control Concept (4th Generation Submarine Combat Management System (CMS))" has been developed based on business processes. In the concept, the scope of the Combat System is not limited to operations and weapon systems and sensors; rather, the entire submarine is considered as the Combat System. There has been a shift from the device-based approach in 3rd Generation CMS to a function and service-oriented approach, and the capabilities of "Operational Intelligence" and "Operational Autonomy" have been defined. The 4th Generation Submarine CMS envisions a holistic command and control information system that not only targets detection and the success of torpedo and guided missile (G/M) firings but also ensures the success of the submarine's entire operation. Considering that the approach proposed by the concept aims to reduce and even eliminate the need for human resources, it is assessed that the concept can also be applied to the command and control of unmanned submarines and unmanned underwater vehicles with regard to platform autonomy, highlighting potential areas for application. Focus on "human factors" in layouts and console designs in the submarine control room, and UUV command centers improves teamwork and business processes. The "Submarine CIC Orchestration" design meets human factors requirements and complements the physical dimension of the 4th Generation CMS concept. The concept is applicable to other platform CMSs, unmanned surface vehicles (USV), and operational level C2ISs.

In recent years, there is a significant increase in the number of devices with low power consumption. The energy requirements of these devices are provided by chemical batteries. The batteries must be charged at regular times, and cause some problems such as environmental pollution. RF energy harvesters are an alternative energy source for the batteries. In this study, the responses of 900 MHz RF energy harvester, which was previously tested, are estimated using an Artificial Neural Network (ANN) method in different states. For this aim, the output power values are determined by using the input power and the frequency of the signal and the load resistances connected to the energy harvester.

The article examines the influence of modern technologies on classical art of painting. Examples of the use of technologies in the works of famous artists are given. Changes in compositional means under the influence of technologies are considered, as well as examples of new painting means that were introduced thanks to modern technologies. Against the background of the introduction of new tools in creativity, the practical aspect of teaching painting using digital tools for students majoring in design in higher education institutions is considered.

As witnessed by the developed countries, rural electrification (RE) is of paramount importance for the development of a nation. It serves many social and economic purposes. Since the majority of the people in developing countries live in the rural areas, there is a need for these nations to address themselves to the real problems of rural electrification. The dominant method used for lighting up these areas is national grid extension which quite often proves to be very expensive due to the dispersed nature of rural population distribution and very low power consumption Diesel generators have also been used to serve remote areas but these are not very appropriate because they require skilled maintenance and their running costs are very high especially where fuel is imported. Even the oil producing countries do seek alternatives due to the realization that oil is a depletable resource Cheaper and more appropriate technologies for lighting up the rural areas must therefore be sought and most countries are turning to renewable resources. O f all the renewable sources, mini/micro hydroelectric power (MHP) has been found to be the cheapest and moreover, it raises very little environmental concern. In this study an evaluation model for MHP has been developed for applications in Kenya. This model performs the technical design of plants o f capacity up to 500kW. It is based on relevant mathematical formulae and engineering considerations applicable to hydro power plants of this range The power demand o f the potential consumers should be estimated as a pre-condition for the use of the model. The model matches the discharge rate corresponding to the potential power demand and the river discharge rate to determine the design discharge rate. Where the demand surpasses the supply, a 70 percent exceedance design discharge rate is recommended. Storage reservoirs have been found to increase the project cost drastically due to the fact that a dam would have to be constructed and the excavation work involved. They should therefore be avoided. v x <. The study concentrates on A C. generation, single phase for plants upto lOkW and three phase above this rating. The choice of an induction generator can be made whenever it vu costs less than the equivalent synchronous generator which has been found to be upto 25kW. For plants of capacity up to 200kW, Electronic Load Controllers (ELC) may be used with synchronous generators and beyond 200kW a speed governor is recommended. The use of a step-up transformer is only considered when the power to be transmitted exceeds 40kW. The effect of interest rate charged on capital and discount rate have also been investigated and it has been found that the higher these rates are, the lower the Net Present Worth (NPW) of a project becomes. The unit cost of generation of power decreases as the annual capacity factor improves and as the useful life of a project increases. Unit cost of diesel generation was found to be higher than MHP for values of capacity factor above 17.5% for the plant studied Although the unit cost of generation for both MHP and diesel decreases individually as the annual capacity factor improves, the difference in cost between the two sources widens as the annual capacity factor improves.

Новый этап в области компьютерных технологий часто называют очередной «весной искусственного интеллекта». Её начало обычно отсчитывают с момента появления нейронной сети, сегодня известной под названием AlexNet, успех которой в распознавании зрительных образов возвестил о начале «революции глубокого обучения». В результате этой революции машинам удалось превзойти человеческий уровень при решении множества задач. Сегодня уже мало кого удивляют победы машин над сильнейшими игроками в го, создание ими музыки и картин, предсказание нейронными сетями пространственной структуры белков и другие вещи, которые десять лет назад мы посчитали бы чудесами. Алгоритмы искусственного интеллекта (ИИ) быстро вошли в нашу жизнь и стали её неотъемлемой частью. Например, каждый раз, когда вы делаете фотографию при помощи смартфона, её обработку выполняет нейронная сеть.

На смену весне искусственного интеллекта приходит лето. Эта книга рассказывает о том, какие события в истории науки привели к началу этого лета, о современных технологиях ИИ и их возможностях, а также пытается приоткрыть завесу, скрывающую от нас мир ближайшего будущего.

Мифы и заблуждения об искусственном интеллекте, страхи, насущные проблемы, перспективные направления исследований — обо всём этом вы узнаете из «Большой книги искусственного интеллекта».

An overview of Hydropower application to the waste water (Sewage) is described here. Firstly the waste water treated in Sewage Treatment plant. It includes physical, chemical and biological contaminants. Its objective is to produce an environmentally. Here the solid waste (treated sludge) suitable for disposal of reuse (usually for fertilizers) and treated water used for generating electricity by a small Hydro power plant. Treated water of sewage at a high pressure or flowing with a high velocity can be used to run turbine or water wheel coupled to Generator and therefore of electrical power is becoming more and more popular as it is reliable and requires least maintenance and care. Output power has been estimated for available different head and flow rate of the waste water. Various types of the turbine-generator sets for different available head have also suggested for reliable operation of the developed this plant. The main advantage of this power plant is an independent power pl...

The study explored the use of artificial intelligence (AI) model for dynamic spectrum allocation in cellular networks. It aims to optimise spectrum allocation and address static issues by adaptively allocating frequency bands based on real-time demand. The research examines conventional methods of allocating spectrum and presents AI as a potential solution to spectrum shortage problems. Deep-Q algorithm was chosen to develop the model, which is built as a framework of components, system architecture, and methods that could be integrated into existing networks to facilitate dynamic spectrum allocation. This approach enhances realtime spectrum allocation and dynamic decision-making to adaptively assignment frequency. The model was evaluated for its throughput, spectrum efficiency of available spectrum management and other metrics in both simulated scenarios by representing various network topologies and traffic situations, as well as real-life network. It was observed that the model exhibits 88 percent efficiency in intelligently managing available spectrum. This shows that AI-driven dynamic spectrum allocation is far advantageous compared to conventional static allocation approach with limitations, and other factors. The achievement of 88 percent spectrum allocation through our AI-driven dynamic spectrum allocation, shows that the study contributed to the body of knowledge on cellular network management.

Ionic liquids analogues know as Deep Eutectic Solvents (DESs) are gaining a surge of interest by the scientific community and many applications involving DESs have been realized. Moisture content is one of the important factors that affects the physical and chemical characteristics of these fluids. In this work, the effect of mixing water with three common type III DESs on their viscosity was investigated within the water mol fraction range of (0-1) and at the temperature range (298.15-353.15 K). Similar trends of viscosity variation with respect to molar composition and temperature were observed for the three studied systems. Due to the asymmetric geometry of the constituting molecules in these fluids, their viscosity could not be modelled effectively by the conventional Grunberg and Nissan model, and the Feng-He model was used to address this issue with excellent performance. All studied aqueous DES mixtures showed negative deviation in viscosity as compared to ideal mixtures. The degree of intermolecular interactions with water reaches a maximum at a composition of 30% aqueous DES solution. Reline, the most studied DES in the literature, showed the highest deviation. The information presented in this work on the viscosity of aqueous DES solutions may serve in tuning this important property for diverse industrial applications involving these novel fluids in fluid flow, chemical reactions, liquid-liquid separation and many more.

A comparative study for the COD removal of Chlorpyrifos, Fenitrothion (3%) and Acetamiprid (20%) by electrocoagulation process was performed. The effect of various parameters of electrocoagulation (EC) on removal efficiency was studied and optimized. The COD removal using nonpolar iron electrodes was affected by current density, contact time, initial pH and initial concentration of pesticides. The optimum conditions for the electrocoagulation process were identified as contact time (4, 5 and 10 min), for a maximum abatement of 100 mg/L pesticide solution, respectively. The results provide an important idea for the development electrocoagulation process to remove pesticides significantly along with COD removal using moderate iron concentration, thereby lowering the cost of treatment. Moreover, results show that the pesticide was removal quickly with a maximum rate of 87% for the contact time of 4 min, which revealed that pesticides wastewater can be treated using electrocoagulation.

Hydropower is the main energy source in Ethiopia which can meet the energy demand of the people. Although the country has immense amount of hydropower potential, only a fraction of it has been harnessed so far. This is mainly due to the capital intensive nature of hydropower development projects. This study demonstrates the possibility of power production from existing Megech and Ribb projects located in Amhara region, Ethiopia. The discharges were determined from monthly irrigation releases through dam's outlets, monthly excess water releases by spillways and both of them. Gross heads were determined using Global Mapper from Digital Elevation Model and Google Earth, and assessed using RETScreen Expert software to obtain the optimum gross heads. Three alternatives were evaluated for each project. The optimum gross heads of the three alternatives were 39.79 m, 72.1 m & 46.16 m and 38.64 m, 65 m & 77.14 m for Megech and Ribb respectively. The hydropower components for all alternatives of both projects were designed. The hydropower potential of the three alternatives of both projects were assessed and their economic feasibility were evaluated using RETScreen Expert software. The three alternatives of both projects were found feasible with benefit cost ratio of greater than one, net present value of greater than zero and internal return rate of greater than 7%. Moreover, the first alternative is more feasible than the other alternatives for both projects. It was feasible for Megech project with power capacity of 3.72 MW, electricity exported to grid of 12,113 MWh, benefit cost ratio of 10.7, net present value of 8,773,908 USD, simple payback period of 9.3 years and internal return rate of 28.6%. It was also feasible for Ribb project with power capacity of 8.42 MW, electricity exported to grid of 14,311 MWh, benefit cost ratio of 2.4, net present value of 4,137,857 USD, simple payback period of 27.8 years and internal return rate of 11.7%. In addition, using statistical measures the model and general hydropower formula results were evaluated and their results showed that the values using the model and general hydropower formula are related and also indicated that the model performed well. Therefore, the results showed that both projects are feasible and it is worth investing on them. The significant amount of hydropower potential obtained will add an important monetary value to economy of the country.

Hydropower is the main energy source in Ethiopia which can meet the energy demand of the people. Although the country has immense amount of hydropower potential, only a fraction of it has been harnessed so far. This is mainly due to the capital intensive nature of hydropower development projects. This study demonstrates the possibility of power production from existing Megech and Ribb projects located in Amhara region, Ethiopia. The discharges were determined from monthly irrigation releases through dam's outlets, monthly excess water releases by spillways and both of them. Gross heads were determined using Global Mapper from Digital Elevation Model and Google Earth, and assessed using RETScreen Expert software to obtain the optimum gross heads. Three alternatives were evaluated for each project. The optimum gross heads of the three alternatives were 39.79 m, 72.1 m & 46.16 m and 38.64 m, 65 m & 77.14 m for Megech and Ribb respectively. The hydropower components for all alternatives of both projects were designed. The hydropower potential of the three alternatives of both projects were assessed and their economic feasibility were evaluated using RETScreen Expert software. The three alternatives of both projects were found feasible with benefit cost ratio of greater than one, net present value of greater than zero and internal return rate of greater than 7%. Moreover, the first alternative is more feasible than the other alternatives for both projects. It was feasible for Megech project with power capacity of 3.72 MW, electricity exported to grid of 12,113 MWh, benefit cost ratio of 10.7, net present value of 8,773,908 USD, simple payback period of 9.3 years and internal return rate of 28.6%. It was also feasible for Ribb project with power capacity of 8.42 MW, electricity exported to grid of 14,311 MWh, benefit cost ratio of 2.4, net present value of 4,137,857 USD, simple payback period of 27.8 years and internal return rate of 11.7%. In addition, using statistical measures the model and general hydropower formula results were evaluated and their results showed that the values using the model and general hydropower formula are related and also indicated that the model performed well. Therefore, the results showed that both projects are feasible and it is worth investing on them. The significant amount of hydropower potential obtained will add an important monetary value to economy of the country.

Prediction of surface tension of highly non-ideal binary aqueous-organic mixtures is crucial for interpreting the interaction between the molecules. In this regard, a multi-layer perceptron (MLP) artificial neural network (ANN) model is developed to predict the binary aqueous-organic surface tension as a function of mixture composition and temperature while the organic compounds are very dissimilar in size and type. To correlate the binary surface tension, gathered experimental surface tension data consisted of 30 binary mixtures containing 2271 data points in the wide temperature range of 273-471.15 K are randomly divided into three different subsets namely training (70 % of total data), validation (15 % of total data) and testing (15 % of total data) subsets. Different input variables are examined and the number of hidden neurons is optimized. The obtained results revealed that it is possible to correlate the binary surface tension with the best MLP network with 27 neurons in the hidden layer and inputs variables of temperature, mole fraction, molecular weight and critical pressure of non-water component with the average absolute relative deviation (AARD %) of lower than 1.43 %. Comparison of accuracy of the MLP model with several common models such as Jouyban-Acree model, Wilson equation, Paquette and Rasmussen areas and several equations of state including SRK, PR and CPA revealed more accuracy of the proposed MLP based model.

Проблема памяти в цифровой культуре привлекает сегодня многих медиаартистов, стремящихся к художественному осмыслению тенденций современности. Желание постичь и визуально представить, как работает память в эпоху доминирования машинных алгоритмов и цифровых архивов, рождается из осознания произошедших качественных изменений в мышлении и практиках современного человека. Тотальная цифровизация не просто меняет работу памяти, но и создает ситуацию, когда различные уровни культуры — от индивидуальных до глобальных — cтановятся полностью от неё зависимыми. Как помнит человек, если новые аппаратные и программные возможности позволяют

This paper presents the operation of centrifugal pumps as turbines for micro hydropower application. Block diagrams are used to present components required from intake chamber to hydropower plant and their electrical energy mechanism for micro hydropower system. A comparison of operational parameters of pump as turbine and conventional turbines is discussed. Also, the paper discusses three types of methods for selecting the centrifugal pump as turbine based on the head, flow rate, rotational speed and specific speed. Furthermore, the technique used to determine the flow rate characteristics are discussed as well as the technical guideline is tabulated to assist the application of centrifugal pump as turbine for micro hydropower scheme development.

This paper presents the operation of centrifugal pumps as turbines for micro hydropower application. Block diagrams are used to present components required from intake chamber to hydropower plant and their electrical energy mechanism for micro hydropower system. A comparison of operational parameters of pump as turbine and conventional turbines is discussed. Also, the paper discusses three types of methods for selecting the centrifugal pump as turbine based on the head, flow rate, rotational speed and specific speed. Furthermore, the technique used to determine the flow rate characteristics are discussed as well as the technical guideline is tabulated to assist the application of centrifugal pump as turbine for micro hydropower scheme development.

Что такое нейросеть Обязательные требования к работе нейросети: ■ Любую нейросеть необходимо предварительно обучить, т.е создать нужную конфигурацию связей ■ Любая нейросеть дает вероятностный ответ, поэтому один и тот же запрос к нейросети может давать разный результат

KNN is one of the most popular classification methods, but it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous class-irrelevant features. Linear feature transformation methods have been widely applied to extract class-relevant information to improve kNN classification, which is very limited in many applications. Kernels have been used to learn powerful non-linear feature transformations, but these methods fail to scale to large datasets. In this paper, we present a scalable non-linear feature mapping method based on a deep neural network pretrained with restricted boltzmann machines for improving kNN classification in a large-margin framework, which we call DNet-kNN. DNet-kNN can be used for both classification and for supervised dimensionality reduction. The experimental results on two benchmark handwritten digit datasets show that DNet-kNN has much better performance than largemargin kNN using a linear mapping and kNN based on a deep autoencoder pretrained with retricted boltzmann machines.

Until now, the energy needs of all countries have started to run out and each country is competing to find alternative energy sources as a substitute for energy sources for future needs. In Indonesia, as an archipelagic country with many rivers, it is actually easy to get an environmentally friendly alternative energy source that becomes a pilot project for a pico-hydro power plant that is useful for replacing energy sources from fossils. The pilot project of a pico-hydro Scale Power Plant Utilizing River Water Flows as Renewable Energy needs to be continuously studied and developed by all parties. The purpose of this study was to obtain an overview of the usability of river flows that can be used as a picohydro scale generator application. The research method was carried out by field surveys, making a study of river flow potential for picohydro applications. From the results of this study, information was obtained that in every river flow that flows water with a constant flow, the installation of pico-hydro scale plants can be used, because this condition will affect the operational conditions of the generating equipment, such as turbine rotation, power generated, pico-hydro efficiency up to the application in the form of the resulting load will be a problem if the river flow used in this pico-hydro application changes or is not constant, especially when the water flow becomes smaller so that it cannot turn the turbine during the dry season. The results of the survey and analysis of potential studies at a cross-sectional area of 2.15 cm obtained a river water flow velocity of 2.46 m/sec with an average measurement discharge of 3.26 m³/second, so that if it is estimated that the potential power that can be generated reaches 4.57 kW .

Load Frequency Control (LFC) is an issue of top importance to ensure microgrids (MGs) safe and reliable operation in AC MGs. Primary frequency control of each energy source can be guaranteed in AC MGs in order to integrate other energy sources. This paper proposes a micro-hydro frequency control scheme, combining the control of a reduced dump load and the nozzle flow control of Pelton turbines operating in isolated from electricity grid. Some researchs have reported the integration of dump load and flow control methods, but they did not reduce the dump load value and adjust the nozzle flow linearly to the power value demanded by users, causing the inefficient use of water. Simulation results were obtained in Matlab®/Simulink® using models obtained from previous research and proven by means of experimental studies. The simulation of the proposed scheme shows that instantaneous frequency variation is about 2 % even for perturbations magnitude up to 12 %. The validation result shows a ...

Small and mini hydropower systems demonstrate an attractive solution with the help of pump as turbine (PAT) for hydropower generation with lower costs and less impact on the environment. African is having a major challenge in connection with desires for energy. There is the need to increase energy security particularly in the rural areas. The rapid growth of renewable energy capacity in the region contains all the potentials that can answer to quite a lot of these problems. Small-scale hydropower is one of the most cost-effective energy technologies to be considered for electrification in the region since it is economical with other significant benefit if implemented. This paper focuses on the PAT for small-hydropower to Africa since the establishment of widespread small hydropower by the use of PAT can help overcome the current power crisis and play a role in the economic progress of the Region. The paper draws attention on the importance of implementing Pump as turbine for small hydropower development in Africa. It highlights on hydropower potential sites in the region, challenges, the characteristics of pump as turbine has been discussed and draw conclusion on PAT for small scale hydropower development as one of the most cost-effective solution

Many people in Nigeria and other countries of the world depend on cassava products as their major source of food. Forecasting the yield of cassava makes farmers and agricultural stakeholders proactive in strategic planning towards its availability for the teaming population. The complexities of Cassava Yield Forecasting (CYF) incapacitate linear models in producing accurate results. Sequel to the necessity of comprehending the patterns, influence and parameter combination for optimal cassava yield forecasting, this work proposes Artificial Neural Network (ANN) paradigm with abilities to learn from previous yield parameters and generalize into unseen patterns. ANN model for CYF was designed and furnished with hyperbolic transfer function in the hidden layers while Sigmoid transfer function was deployed in the output layer. The cassava dataset with 2500 samples was collected and used for training and testing of the model. Least training and testing errors of 0.0013 and 0.0259 respectively were observed in the ANN model. Investigations regarding cassava yield based on the number of cassava cultivars planted per stand revealed that the cultivation of one stem per stand had a higher contributory effect on average cassava yield than cultivation of more stems per stand. The ANN model earned 94.80% and 96.10% for accuracy and precision metrics respectively, indicating acceptable performance. Detailed ANN investigations of cassava yield patterns in response to cassava varieties, soil types, fertilizer types, weather conditions and cultural practices as well as intelligent computational techniques for selection of ANN optimal training parameters are recommended for further research with a view to improving cassava yield forecasting.

Most of the tanneries in Ethiopia (90%) do not treat and very few (10%) partially treat their effluent before discharging it into the receiving water bodies. The untreated tannery effluent causes tremendous pollution of water resources in the country, especially due to its high organic loading and chromium content. Chromium(III) salts are the most widely used chemicals for tanning processes, but only about 60-70% of total chromium salts reacts with the hides and about 30-40% of the chromium remain in the solids and liquid wastes (especially spent tanning solutions). Therefore, the removal and recovery of the chromium content of these wastewaters is necessary for environmental protection and economic reasons. The purpose of this study was to develop an alternative process for removing and recovering trivalent chromium from tannery wastewater via chemical precipitation with sodium hydroxide (NaOH), calcium hydroxide (Ca(OH)2) and magnesium oxide (MgO). The effects of pH, stirring time...

Аннотация Цель. Построение компьютерной модели работы нейронной сети для распознавания объектов. Процедура и методы. На основании идей, положенных в основу теории распознавания и теории нейронных сетей, построена модель работы нейросети, предназначенной для распознавания исследуемых отображений с заданной точностью. Для успешной работы нейронной сети привлекались базы данных в открытом доступе с удалённых серверов. Это позволило использовать нейросети в стеснённых условиях (в отсутствии мощных вычислительных машин). Для организации работы и управления нейронной сетью разработана программа на языке Python. Результаты. Построена нейронная сеть, распознающая исследуемые отображения с заданной точностью. Для управления построенной нейросетью и привлечения массивов баз данных с удалённых серверов разработана программа на языке Python. Продемонстрирован принцип работы нейросети на практике на примере распознавания изучаемых отображений. Практическая значимость. Модель даёт реальный рецепт построения нейронной сети и использования её на практике в условиях отсутствия мощной вычислительной машины.

Theoretical analysis of micro-hydropower technology for electricity generation. A case study of Hhaynu micro-hydropower plant in Tanzania Article 3 Title: Dynamic modelling and simulation of a micro-hydro turbine system with hydrogen fuelled internal combustion engine for supplying electricity to off-grid rural community. A case study of Hhaynu micro-hydropower plant in Tanzania Article 4 Title: Optimization model of a micro-hydro turbine power supply to consumer load demand for off-grid rural community. A case study of Hhaynu micro-hydropower plant in Tanzania Article 5 Title: Design and simulation of a micro-hydro turbine system for electricity supply and hydrogen production in off-grid rural community of Tanzania

Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a theoretical model with and without random noise in order to study the effect of noise on the technique and then extended to real field data. It is noted that the method under discussion ensures fairly accurate results even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana, India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between the measured and predicted parameters.

This paper presents the performance of a radial discharge centrifugal pump operated in turbine mode. Increasing environmental concerns and depleting fossil fuel reserves has shifted the focus of the world to explore various possible renewable energy alternatives for energy generation. There are many villages in developing countries which are still deprived of electricity. The quality of life in these villages definitely suffers. Hydro power is the cheapest and clean renewable energy source. The electricity generated at large hydro power projects is transmitted through the grid system. The high terrain remote villages where grid supply is not feasible due to economical reasons remain deprived of electricity. Many of these high terrain remote village populations cannot afford expensive technology for standalone power generation. Amongst these villages, those which are blessed with natural streams of water and rivers, standalone hydro power generation is the best option. The cost of co...

Hodgkin-type lymphoma is a disease with unique histological, immunophenotypic, and clinical features. This disease occurs in nearly 30% of all lymphomas. Its treatable is high. However, the treatment plan is specified after the stage and risk status are determined. For this reason, it is an important process for doctors to decide on the stage of the disease correctly. Some of the data used for this decision are the patient's history, detailed physical examination, laboratory findings, imaging methods and bone marrow biopsy results. Hybrid FDG-PET is an important imaging method used in the medical world. This method is used in diagnosis, evaluation of response given to treatment, staging and restaging process. However, it is radiation-based. Therefore it has the possibility of producing undesirable results in the future. In this study, an artificial intelligence-based computer-assisted decision support system is done to reduce the number of used medical methods and radiation exposure. Data were obtained from the NCBI-GEO dataset. The evaluation of these data, which contains missing values, is handled in two ways. Firstly, samples with missing values in the initial evaluation are deleted from the dataset. Then, these data are trained with "trainlm" function in artificial neural network architecture. However, reducing the error value of the estimates is important. For this, the artificial neural network architecture is retrained with the artificial bee colony algorithm, particle swarm optimization algorithm and invasive weed algorithm, respectively. Secondly, the same operations are performed again on the dataset containing missing values. As a result of the training, the maximum performance was obtained for invasive weed and particle swarm optimization algorithms with 1,45547E+14 and 1,23103E+14 average error rates, respectively.

The aim of this research is to develop and propose a single-layer semi-supervised feed forward neural network clustering method with one epoch training in order to solve the problems of low training speed, accuracy and high time and memory complexities of clustering. A code book of non-random weights is learned through the input data directly. Then, the best match weight (BMW) vector is mined from the code book, and consequently an exclusive total threshold of each input data is calculated based on the BMW vector. The input data are clustered based on their exclusive total thresholds. Finally, the method assigns a class label to each input data by using a K-step activation function for comparing the total thresholds of the training set and the test set. The class label of other unlabeled and unknown input test data are predicted based on their clusters or trial and error technique, and the number of clusters and density of each cluster are updated. In order to evaluate the results , the proposed method was used to cluster five datasets, namely thebreast cancer Wisconsin, Iris, Spam, Arcene and Yeast from the University of California Irvin (UCI) repository and a breast cancer dataset from the University of Malaya Medical center (UMMC), and their results were compared with the results of the several related methods. The experimental results show the superiority of the proposed method.

COVID-19 has changed the way we live, communicate and work, as well as altering our feelings. The higher education sector, alongside other sectors, has been severely affected by the pandemic and its serious repercussions. Academic and teaching staff have had to work from home and convert to online teaching, a change which has been met with both negative and positive feelings. The need for new competencies and upskilling, among other challenges, has been encountered. Therefore, the objectives of this study are aligned with exploring the impact of three constructs—self-efficacy, positive feelings and negative feelings—on the performance of academic and teaching staff at public and private universities in Bahrain during the COVID-19 lockdown. Additionally, the impact of self-efficacy on these feelings was explored. A cross-sectional quantitative survey instrument was developed, validated and distributed using 83 valid responses. A two-way approach was followed to evaluate the model usi...

One of the biggest challenges facing the world today is to provide access to a safe and affordable electricity supply. Depending on the river flow, small-hydropower is often a cost-effective source of renewable energy. Egypt is home to part of Africa's longest river and it is a relatively reliable source. Many small hydropower plants can be installed in run-of-river schemes or implemented in existing river infrastructure. We argue that it is essential for the Egyptian government to make use of hydropower resources, not only to meet the increasing demand but also to reduce fossil fuel use, and associated environmental pollution. This paper investigates the small hydro energy potential at selected locations in Nile Delta-Egypt and presents a feasibility study of small hydropower for these locations. The head and water flow rates for the past five years are used to find out the hydro energy potential. The annual energy of three different small hydro turbines is calculated for eight selected regions in Nile Delta-Egypt. The analysis includes a comparison between energy outputs from the three hydro turbines types. Furthermore, a MATLAB program is built to compute the efficiency of the studied turbines in each site at different heads and water flow rates. It is found that the use of Crossflow and Kaplan turbines with different sizes can enhance electric energy production at the selected sites.

COVID-19 has impacted Higher Education worldwide. While several studies have examined the effects of the pandemic on students, few have addressed its impact on academic staff. Here, we present both survey (n = 89) and interview (n = 12) data highlighting the pandemic-induced effects on academics from various disciplines and career stages. Data was collected between May and September 2020, aiming to capture and understand the immediate effects of the U.K. lockdown on the academics examining demographic and employment factors, digital abilities and confidence, and mental wellbeing. Analyses revealed that most academics were satisfied with the support they received from the university and colleagues, and they had adequate equipment and space at home to work. However, half incurred additional financial costs to maintain access to technology and many felt an altered relationship with the university. There were discrepancies in digital abilities and confidence according to employment stat...

In any water system which has excessive available energy (e.g. natural falls, irrigation systems, water supply, sewage or rain systems), the application of a pump instead of a turbine, for energy production, seems to be an alternative solution with easy implementation and considerable equipment cost savings. Micro-hydropower corresponds to a typical renewable energy source without any relevant impacts, and has multiple advantages, as a decentralised, low-cost and reliable form of energy. Unconventional solutions are in the forefront of many developing countries to achieve energy self-suciency.

In this paper, we implemented a speaker-dependent speech recognition system for 11 standard Arabic isolated words. During the feature extraction phase, several techniques were used such as Mel frequency cepstral coefficients, perceptual linear prediction, relative perceptual linear prediction and their first order temporal derivatives. Principal component analysis was adopted in order to reduce the feature dimension. The recognition phase is based on the feed forward back-propagation neural network using two learning algorithms: the Levenberg-Marquardt "Trainlm" and the scaled conjugate gradient "Trainscg". Hybrid approaches were used and compared in terms of computational time and recognition rates and have produced very interesting performances.

KNN is one of the most popular data mining methods for classification, but it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous class-irrelevant features. Linear feature transformation methods have been widely applied to extract classrelevant information to improve kNN classification, which is very limited in many applications. Kernels have also been used to learn powerful non-linear feature transformations, but these methods fail to scale to large datasets. In this paper, we present a scalable non-linear feature mapping method based on a deep neural network pretrained with Restricted Boltzmann Machines for improving kNN classification in a large-margin framework, which we call DNet-kNN. DNet-kNN can be used for both classification and for supervised dimensionality reduction. The experimental results on two benchmark handwritten digit datasets and one newsgroup text dataset show that DNet-kNN has much better performance than large-margin kNN using a linear mapping and kNN based on a deep autoencoder pretrained with Restricted Boltzmann Machines.

Determining good initial conditions for an algorithm used to train a neural network is considered a parameter estimation problem dealing with uncertainty about the initial weights. Interval Analysis approaches model uncertainty in parameter estimation problems using intervals and formulating tolerance problems. Solving a tolerance problem is defining lower and upper bounds of the intervals so that the system functionality is guaranteed within predefined limits. The aim of this paper is to show how the problem of determining the initial weight intervals of a neural network can be defined in terms of solving a linear interval tolerance problem. The proposed Linear Interval Tolerance Approach copes with uncertainty about the initial weights without any previous knowledge or specific assumptions on the input data as required by approaches such as fuzzy sets or rough sets. The proposed method is tested on a number of well known benchmarks for neural networks trained with the back-propagation family of algorithms. Its efficiency is evaluated with regards to standard performance measures and the results obtained are compared against results of a number of well known and established initialization methods. These results provide credible evidence that the proposed method outperforms classical weight initialization methods.

In this study, a new approach that is closer to the real turbine without abandoning the advantages of nonlinear modeling simplicity compromising the simplicity of the nonlinear model is proposed. The purpose of the study is to contribute to making theoretical studies of microhydroelectric power plants (MHPPs) more realistic. Studies on their control, especially frequency control, have increased with the expansion of grid-connected power plants. However, some problems have occurred when converting simulation studies of the frequency control to practical studies. The reasons for these problems are that the systems cannot be modeled completely. In this study, first, the nonlinear model equations of a Pelton turbine are primarily constituted. The efficiency curve of the turbine is then obtained experimentally and added to the nonlinear model. The simulation results are compared with the data obtained from the prototype MHPP with a Pelton turbine developed in the laboratory. Finally, it is seen that the proposed method is successful. Thus, with the help of the proposed method, theoretical studies that researchers do can be easily converted into practical applications.

Electricity is an important ingredient for socioeconomic development of a society. In households electricity has an impact on living standard, health, education to mention but a few. However, access to it in rural areas has been difficult in many African countries due to different reasons, cost being one of them. This paper proposes a low-cost small hydropower plant that can benefit communities in rural areas which are endowed with small hydropower resources. The plant employs a synchronous–induction generator combination in a power generation scheme.

ABSRACT Sliding mode control has received much attention due to its major advantages such as guaranteed stability, robustness against parameter variations, fast dynamic response and simplicity in the implementation and therefore has been widely applied to control nonlinear systems. This paper discus the sliding mode control technic for controlling hydropower system and generalized a model which can be used to simulate a hydro power plant using MATLAB/SIMULINK. This system consist hydro turbine connected to a generator coaxially, which is connected to grid. Simulation of the system can be done using various simulation tools, but SIMULINK is preferred because of simplicity and useful basic function blocks. The Simulink program is used to obtain the systematic dynamic model of the system and testing the operation with different PID controllers, SMC controller with additional integral action.

Electricity is an important ingredient for socioeconomic development of a society. In households electricity has an impact on living standard, health, education to mention but a few. However, access to it in rural areas has been difficult in many African countries due to different reasons, cost being one of them. This paper proposes a low-cost small hydropower plant that can benefit communities in rural areas which are endowed with small hydropower resources. The plant employs a synchronous–induction generator combination in a power generation scheme.

The human demand for electricity and fresh water is ever increasing. Due to the nature of hydropower and its economic, social, and environmental benefits, hydropower will be an important contributor to the energy mix of the future. Isolated areas, severely underdeveloped regions, disaster-stricken zones, and remote military outposts have one common need: readily available and efficient means of power generation. The most effective manner to address these needs involves the use of power generating modules that utilize the locally available renewable energy resources. The general concept of a hydroelectric power plant to convert the kinetic and potential energy of the water (mass) moving with some speed (momentum) into a usable form of energy. Hydro power projects involve various considerations at different levels of project implementation. To harness the potential, new turbines have been developed and commercially available. For the cost effective and efficient project we need to stu...

The paper presents the results of the research on the influence of the COVID-19 pandemic on the dissemination of innovative e-learning tools in higher education. Research was carried out in Poland in December 2021 on a sample of 621 students. The main issue that was the subject of the author’s analysis was the influence of the COVID-19 pandemic on the change in the use of innovative e-learning tools in university education. After conducting the research and discussing this and related research about e-learning during the pandemic, it was concluded that the percentage of students familiar with the analyzed e-learning tools has increased significantly during the pandemic. There has been a visible rise, especially in the usage of the following tools: MS Teams, Zoom, and Google Classroom. The most frequently used e-learning tools during the COVID-19 pandemic have been mainly videoconferencing tools such as MS Teams and Zoom. However, students also have used e-learning platforms and e-ma...

Micro-hydro Power Plant had three primary components: water (as the energy resource), turbine, and generator. Water that flew in a specific capacity was channeled from a certain height to the installation house (turbine house). In the powerhouse, the water installation pounded the turbine that made the turbine received direct energy from the water and turn it into mechanical energy and caused the turbine shaft to spin. Changes in the loading could cause fluctuation in the system’s frequency and voltage. This problem could damage electrical equipment. Therefore, the Automatic Voltage Regulator (AVR) was used to control and stabilize the voltage. This research used PID controller to obtain the optimized control parameter in the Micro-hydro Power Plant. This research compared the simulations of without control method, with PID-ZN control method, and with PID-DE method to obtain the best control method. The comparison simulations showed that the best response in the micro-hydro plant an...

Supplemental material, sj-pdf-1-pia-10.1177_0957650921997202 for Classical hydraulic ram pump performance in comparison with modern hydro-turbine pumps for low drive heads by Siddhi Kesharwani, Kupulwng Tripura and Punit Singh in Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy

This study presents an intelligent approach for load frequency control (LFC) of small hydropower plants (SHPs). The approach which is based on fuzzy logic (FL), takes into account the non-linearity of SHPs-something which is not possible using traditional controllers. Most intelligent methods use twoinput fuzzy controllers, but because such controllers are expensive, there is economic interest in the relatively cheaper single-input controllers. A nonlinear control model based on one-input fuzzy logic PI (FLPI) controller was developed and applied to control the non-linear SHP. Using MATLAB/Simulink SimScape, the SHP was simulated with linear and non-linear plant models. The performance of the FLPI controller was investigated and compared with that of the conventional PI/PID controller. Results show that the settling time for the FLPI controller is about 8 times shorter; while the overshoot is about 15 times smaller compared to the conventional PI/PID controller. Therefore, the FLPI controller performs better than the conventional PI/PID controller not only in meeting the LFC control objective but also in ensuring increased dynamic stability of SHPs.

RESUMO: Este artigo visa problematizar a execucao da politica publica do socioeducativo, sobretudo mediante a ramificacao das faccoes criminosas nos centros educacionais de Fortaleza-Ce. A partir da denominada crise do sistema socioeducativo nos anos 2000, houve uma reconfiguracao na dinâmica dos centros educacionais, sobretudo com a ramificacao das faccoes criminosas. Jovens internos foram assassinados nas unidades, constantes rebelioes, ameacas e torturas tecem o cotidiano dessas instituicoes. Os centros educacionais deveriam, a partir de um conjunto de acoes, garantir um processo de responsabilizacao do ato infracional cometido pelo jovem, porem o contexto dessas unidades se apresenta de forma complexa, desumana e violenta. Compreendemos que a politica publica do sistema socioeducativo perpassa pela violacao de direitos sociais dos jovens internos, e acirra o contexto de violencia intensificando a perspectiva da punicao nos centros educacionais. Para a construcao deste trabalho, ...

The hydroelectric plants flow rate always varies with time due to the speed rotation of turbines which affect the amplitude and frequency of electrical energy generated.  Hydro plants are being utilized for the purpose of peaking as well as base load, pumped storage and spinning reserve power operation etc. Especially in a system consisting of large industries, where frequency and voltage fluctuations are required to be kept minimum, their stability determines the quality of power. For efficient use of plant, complex control techniques are employed in the station automation and these involve the turbine governor in control features for which a flexible governor design is essential. MATLAB 2007 Software was used to carry out simulations analysis to develop fuzzy logic controller for hydropower Generator speed regulation with aims of stabilizing output power supply in order to increase the water flows rate through the hydropower penstock. The result of the research shows the hydropowe...

is currently completing his Master's of Applied Science degree in Environmental Engineering and International Development at the University of Guelph. Prior to this he was an engineering consultant for an environmental firm consulting on wind load effects on the built environment for global landmark projects. He lives happily in Guelph, Canada with his wife and two daughters.

Climate change and environmental degradation has resulted in a reduction in water inflow at hydropower plants, as well as a decrease in reservoir levels. Existing hydropower plants suffer from water head reduction, mainly with decrease in efficiency of energy conversion in hydro turbines. This paper showcases the benefits of operations with variable speed in existing hydropower plants, when working at a lower water head than the rated one. Theoretical analyses and tests were performed in a special constructed laboratorial setup aiming at evaluating the amount of efficiency recovery with variable speed operation. Connection alternatives for a constant frequency grid and applications of the learned concepts in an existent hydropower plant are presented. The investigations were applied to the Furnas hydropower plant. The results point out that economic feasibility of the application can be achieved.