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An increasing number of municipalities are striving for energy autonomy. This study determines in which municipalities and at what additional cost energy autonomy is feasible for a case study of Germany. An existing municipal energy system optimization model is extended to include the industrial, commercial and personal transport sectors. Multiple regression methods are benchmarked in order to identify the model best suited for the transfer of individual optimization results to a large proportion of German municipalities. The resulting levelized cost of energy (LCOE) from the optimization of representative case study municipalities are transferred using energy-relevant indicators. The study demonstrates that energy autonomy is technically feasible in 6,314 (56%) municipalities. Thereby, the LCOEs increase in the autonomous case on average by 0.41 /kWh compared to the minimum cost scenario. Apart from energy demand, base-load-capable bioenergy and deep geothermal energy appear to have the greatest influence on the LCOEs. Overall, it appears that municipal energy autonomy is not economically viable under current framework conditions. This study represents a starting point for defining possible scenarios in studies of future national energy system or transmission grid expansion planning, which for the first time consider completely energy autonomous municipalities.

Thermoacoustics is about conversion between thermal and acoustical energies to provide alternative green technology for power cycle and cooling system. The oscillatory flow across porous structure inside the system is playing the role of energy conversion between the acoustic wave and the surface of the porous structure. Better understanding of fluid dynamics of oscillatory flow inside thermoacoustic system is therefore important for the thermoacoustic based energy conversion system. This paper presents the ‘entrance’ and ‘exit’ effects of the oscillatory flow within a parallel-plate structure that is placed inside a standing-wave thermoacoustic environment. Two-dimensional SST k-ω CFD models which were validated using experimental data and theoretical predictions were used for this investigation. Two different operating frequencies of 14.2 Hz and 23.6 Hz were studied for flow with five different amplitudes that were represented using drive ratios of 0.3%, 0.83%, 1.5%, 2.1% and 3%. These correspond to cases with Reynolds numbers between 5936 and 62926. Due to the cyclic nature of the flow, a region defined as an ‘exit’ region was observed in addition to the usual ‘entrance’ region and the fully developed region for flow inside a channel. The change of shape of velocity profiles from the ‘m’ shape profile, to the ‘slug-like’ profile and ‘parabolic-like’ profile was discussed in relation to the ‘entrance’ and ‘exit’ effects on flow inside the channel. The ‘entrance’ and ‘exit’ effects become bigger as drive ratio increases. The effect of ‘entrance’ and ‘exit’ are slightly reducing as frequency increases from 14.2 Hz to 23.6 Hz. This may be related to the shorter travel distance of fluid as the frequency increases. The results shown in this paper suggest that the flow within a 200 mm parallel-plate structure should be treated as developing flow especially for flow with low resonance frequency at drive ratio higher than 1%.

The effects of different crumb rubber (CR) contents and their interactions with high temperature ranges on the rheological behavior of asphalt binder were investigated in this study using the response surface methodology (RSM). A temperature sweep test was performed using a dynamic shear rheometer (DSR) on modified binders with 5, 7, 10, 12, and 15 crumb rubber content at temperatures ranging from 46 °C to 82 °C. RSM statistical analysis was conducted based on central composite design (CCD) and assuming the rheological parameters complex modulus (G*) and phase angle (I´Â°) as response variables. Results from analysis of variance (ANOVA) test indicated that temperature and rubber content affect rheological parameters significantly (p < 0.05). A value of p < 0.0001 indicated a good agreement between the predictions of quadratic models and experimental results with a mean error

Evolutionary multitasking (EMT) is a newly emerging research topic in the community of evolutionary computation, which aims to improve the convergence characteristic across multiple distinct optimization tasks simultaneously by triggering knowledge transfer among them. Unfortunately, most of the existing EMT algorithms are only capable of boosting the optimization performance for homogeneous problems which explicitly share the same (or similar) fitness landscapes. Seldom efforts have been devoted to generalize the EMT for solving heterogeneous problems. A few preliminary studies employ domain adaptation techniques to enhance the transferability between two distinct tasks. However, almost all of these methods encounter a severe issue which is the so-called degradation of intertask mapping. Keeping this in mind, a novel rank loss function for acquiring a superior intertask mapping is proposed in this article. In particular, with an evolutionary-path-based representation model for optimization instance, an analytical solution of affine transformation for bridging the gap between two distinct problems is mathematically derived from the proposed rank loss function. It is worth mentioning that the proposed mapping-based transferability enhancement technique can be seamlessly embedded into an EMT paradigm. Finally, the efficacy of our proposed method against several state-of-the-art EMTs is verified experimentally on a number of synthetic multitasking and many-tasking benchmark problems, as well as a practical case study.

There is increasing interest in kinetic models of catalytic refining processes, because of their importance in developing and improving these processes. In fact, the models can be used to predict process performance parameters and to determine the optimum operating conditions. In this work, a detailed reaction kinetic model was developed to describe light naphtha isomerisation reactions over different Pt/zeolite catalysts (Pt/mordenite, Pt/ZSM5, and Pt/BETA). The proposed kinetic model included 66 isomerisation, 28 ring opening, 1 olefin saturation, 22 dehydrogenation, and 90 hydrocracking reactions for 52 real light naphtha components graded from methane to n-octane. A power law reaction kinetics was assumed for all reactions, and 414 kinetic parameters were fine-tuned by comparing the predictions of the model with the results of light naphtha isomerisation experiments at six different temperatures (290, 310, 330, 350, 370, and 390 °C). The Matlab software (version 2015a) was used to perform all the calculations in the current study, and the optimum set of kinetic parameters was estimated using genetic algorithm optimisation. The mean absolute errors for all component compositions at different temperatures were found to be 0.003608, 0.004841, and 0.004757 for the Pt/mordenite, Pt/ZSM5, and Pt/BETA catalysts, respectively. The values of the mean absolute errors estimated from the comparison between the experimental and predicted results showed a very good agreement between the model predictions and the experimental data.

A comparative evaluation of conventional drilling and helical milling to process holes in AZ31 Magnesium alloy is reported. The results highlight the advantages of helical hole milling over the mechanical drilling process in terms of lower machining forces. Besides, the process is successful in reducing the machining temperature and size of the burrs formed, in addition to improved surface finish. Further, analysis of the helical hole milling process demonstrated that the axial pitch has a dominating influence on the thrust force, whereas the feed value greatly influences the radial force. The spindle speed is found to influence the machining temperature significantly. The chip morphology is found to be dependent on feed and axial pitch, with the feed value influencing the chip segmentation and breakability. The absence of burn marks on the chip surface indicates a lower risk of fire hazard.

Accident prevention on the manufacturing shop floor is essential. This paper aims to develop a proximity warning system capable of reducing accidents on the manufacturing shop floor, by notifying employers of any possible danger within the workplace. The system consists of an ultrasonic sensor, Arduino microcontroller, alarm system and an easy to understand user interface that can detect and alert workers within the danger zone. The system was developed using the Unified Modelling Language, python programming language and the Arduino software. Preliminary test conducted on volunteer participants revealed that the system is easy to use and provides real-time easy-to-understand audio and visual feedback to workers. The system is useful for reducing accidents on shop floors.

In view of the increasing levels of renewable generation across the world, the operational dynamics of power systems are changing. One of the dimensions of this transformational change is the nature of fault ride through behavior of renewable technologies such as wind and the associated differences compared to conventional generation. This paper investigates the impacts of fault ride through behavior of wind farms on the system stability in detail, using full dynamic model of a low inertia island power system. Recorded PMU measurements are used to establish the variations in wind farm behavior during and after a fault. A detailed sensitivity analysis on the active and reactive power injection behavior of wind farms is carried out to establish the component of fault ride through behavior that is most critical to system stability, using the full model of the Ireland and Northern Ireland Power system (Irish power system). Detailed investigation of the mechanics of a fault in a low inertia, wind generation dominated system is carried out to reveal potential technical scarcities. Finally, mitigations for identified scarcities arising from non-ideal fault ride through behavior of wind farms are presented in terms of Transmission System Operator led novel system services.

This paper proposes a novel modeling framework and decomposition-based solution strategy combining stochastic programming (SP) and robust optimization (RO) to deal with multiplex uncertainties in coordinated mid- and long-term power system planning. The problem is formulated as a multi-year generation and transmission planning problem from an independent system operator (ISO)'s perspective to minimize both expansion and operational costs under binary and continuous uncertainties, i.e., system component contingency and load/generation variation. N-k contingencies are captured in RO using the reformulated contingency criteria, while the load/generation uncertainty is considered in SP embedded with RO using operating scenarios generated from the historical data with spatiotemporal correlations. The original hybrid model is highly intractable, but the intractability can be relieved by the proposed decomposition strategy based on the column-and-constraint generation and L- shaped algorithms. We apply our model to perform long-term system planning under extremely high renewable penetration and investigate the case of 100% renewables in long-term planning. Numerical experiments on multi-scale test systems verify the efficacy of the proposed approach.

Concentrator PhotoVoltaics (CPV) may generate considerable heat as a byproduct in addition to power. Using that heat as concentrated solar power (CSP) to thermally desalinate salt water would allow for value addition in arid regions by supplementing produced electricity with fresh water. This paper discusses the primary trade-off between the net electricity amount and the net distilled water amount, both of which are desirable products in a hybrid concentrated photovoltaic and thermal system (CPV-T). While higher temperatures promote freshwater generation, they also cause a quick fall in cell efficiency, reducing the quantity of power generated and causing cell deterioration. The CPV-T receiver, which is partially covered with cells and then supplementary heated in a cell-free portion, is modeled and established. Furthermore, simple-stage and multistage desalination unit models are presented, linked to the receiver, and examined to cope with the reduced temperature limits of this receiver. The results demonstrate a CPV-T receiver's ability to generate both electrical power and freshwater, while also demonstrating that the cell temperature constraint can become quite important beyond 50% of the receiver surface coverage. These first order results establish the system's validity and potential and will aid the system designer in the initial system design.

The large-amplitude free vibrations of an inextensible tapered beam with inertia and static nonlinearities were investigated using the continuous piecewise linearization method (CPLM). The CPLM solution was found to be in excellent agreement with exact numerical solutions and more accurate than other published approximate solutions. The study focuses on the large-amplitude vibrations where the nonlinear effects are strong and the response is characterized by anharmonicity. The present investigation showed that the CPLM solution predicted the anharmonic response due to strong nonlinearity whereas the other published solutions showed large errors. The effects of inertia and static nonlinearities were investigated and it was observed that an increase in the static nonlinear stiffness increases the vibration frequency whereas an increase in inertia nonlinearity reduces vibration frequency. Also, the combined effect of the inertia and static nonlinearities on the large-amplitude vibration response was investigated. It was observed that an increase in the ratio of the static to inertia nonlinearity increases the vibration frequency and vice versa.

Enhancing the torsional capacity of reinforced concrete (RC) beams while maintaining reasonably economic and efficient cross-sections poses a practical challenge in the design of such elements. Thus, developing and testing economically viable reinforcing techniques for enhancing the torsional capacity of RC beams is a significant aspect. Among these techniques is the continuous spiral reinforcement system that has shown promising results in enhancing the torsional capacity of rectangular RC elements. In this study, the role of continuous rectangular spiral stirrups, as a transverse reinforcement with a controllable pitch (P) and inclination angle (θ), in enhancing the torsional behavior of rectangular solid and hollow RC beams has been investigated experimentally. The applied torque was directed to lock the spirals for all specimens. The experimental program of this investigation included testing of ten specimens of normal strength RC beams; two of which were solid with closed and spiral rectangular stirrups. While the rest of specimens were hollow specimens included one beam reinforced by closed stirrups and seven beams reinforced by rectangular spiral stirrups with different reinforcing ratios. Different variables were considered to develop a wide range of the beams’ reinforcing scenarios. This includes stirrups type (conventional closed stirrups and rectangular spiral stirrups), inclined stirrups ratio, pitch, inclination angle and finally the longitudinal reinforcement ratio. In addition, a three-dimensional finite element model using ANSYS-15.0 was developed to examine the capability of the model to capture the observed torsional behavior of the experimentally tested beams. The obtained results showed that using inclined spiral rectangular stirrups in reinforcing RC solid and hollow beams greatly enhanced the torsional capacity by about 16% and 18%, respectively compared to using the conventional closed stirrups. Moreover, the results showed that using that technique leads to higher twist- angles, more ductile failure and increased strain energy by about 27% and 16% for solid and hollow beams, respectively. Lastly, the comparison between the results obtained from numerical model and experimental results showed good agreement and thus the numerical model can be used to extend the experimental work for investigating different reinforcing schemes.

Finding relevant objects in a large repository is a fundamental research problem occurring in many applications, such as: data cleaning, data integration, web search, and information retrieval. Instant type-ahead fuzzy search, where user types her query character by character and find the top-k relevant objects, has become widely involved in many applications because it provides the users with rapid response results and improves the user’s experience. The state-of-the-art algorithms are generally inefficient due to their breadth first search algorithm that results in repeated computations. To this end, we propose a novel depth-oriented instant type-ahead fuzzy search algorithm, that largely avoids repeated computations. The efficiency and effectiveness of the proposed approach are empirically demonstrated using real-world datasets. Experimental results show that our approach is 5–10 times faster than state-of-the-art approaches.

As wearable microelectronics become more ubiquitous, and the size and power requirements of such devices decrease, there is increasing research interest in harnessing power from ambient environmental sources through embedded systems as alternative to battery replacements. Accordingly, a multimodal hybrid piezo-electromagnetic insole energy harvester (PEM-IEH) has been presented in this paper as a means to reclaim the biomechanical energy wasted in the surroundings during daily walking. The hybrid device consists of two piezo-ceramic wafer plates, two magnets, and two wound coils. The designed harvester has been simulated, fabricated and experimentally validated. From the frequency response, the hybrid harvester exhibits four resonant frequencies concentrated around 8, 25, 50, and 51 Hz. A maximum combined power of 1400 µW is generated across the optimal load resistances of upper and lower electromagnetic generators at first resonant frequency (8 Hz) under 0.5 g base acceleration, and a 269 µW peak power is obtained across the optimum load resistances of the upper and lower piezoelectric generators at 4th resonance (51 Hz) under 0.5 g. The miniature (46.8 cm3) and lightweight (43.3 g) harvester was incorporated into the sole of an adult-sized commercial shoe, and has been shown to be able to charge a 100 µF capacitor, up to 2.4 Volt within approximately 10 min of slow jogging. The capacitor charging performance presents a remarkable potential application of the harvester in development of micro-power monitoring sensors and wearable microelectronic gadgets where batteries pose a practical bottleneck.

Pursuing long-term climate stabilisation scenarios subjects power, heat, industry, and transport sectors to undergo fundamental transitions towards fully renewable energy systems. Cost-efficient strategies involve the integration of energy vectors, introducing additional cross-sectoral electricity demand and flexibility for traditional power sectors. When performing cross-sectoral capacity expansion planning (CEP) analyses of low-carbon energy systems for decision making support, the underlying mathematical optimisation problems easily become intractable. To overcome the computational challenges of optimising real-world instances of future energy systems with adequate representations of temporal, spatial, and technological detail, this work investigates different modelling approaches for flexible heat pump systems at the power and heat sector interface of the long-term energy system model SCOPE SD. A comparative case study carried out for a net-neutral decarbonisation scenario in Europe and Germany highlights the implications of using model aggregation techniques. It is shown that the proposed modelling approaches can present computational benefits at only limited cost of result accuracy. However, poorly implemented grouping strategies can lead to high result inaccuracies and even a lack of performance improvements.

In this paper, we propose a multimodal deep quality embedding network (MMDQEN) for affective video content analysis. Specifically, MMDQEN can infer the latent label and label quality from the noisy training samples so that cleaner supervision signals are provided to the DNN-based affective classifier, and a tractable objective for MMDQEN is derived with variational inference and conditional independence assumption. In addition, to avoid model evaluation bias incurred by the annotation noise in the test set, new test sets based on the original LIRIS-ACCEDE database, which we name LIRIS-ACCEDE-RANK, are established where the samples are ranked according to their label uncertainty level, with corresponding evaluation metrics introduced accordingly to further reveal the performance of different models. Experiments conducted on both the LIRIS-ACCEDE and the LIRIS-ACCEDE-RANK datasets demonstrate the effectiveness of the proposed method.

One of the main applications of energy storage systems (ESSs) is transmission and distribution systems cost deferral. Further, ESSs are efficient tools for localized reactive power support, peak shaving, and energy arbitrage. This article proposes an ESSs planning algorithm that includes all previous services. The proposed algorithm increases the distribution company profit and minimizes its future system upgrade cost. For a comprehensive planning algorithm, other options, such as including static VAR compensators (SVCs), feeders upgrade, or adding distributed generators, are considered along with ESSs. Different scenarios are utilized to model the load variation, the renewable resources’ intermittency, and the market price fluctuation. The problem constraints include an ESS dynamic model that reflects the capacity and lifetime limitations. Different battery technologies with different costs and dynamic characteristics are compared. The network power flow model is added to account for the voltage level and feeders’ capacity. Power limits constraints are included for the DGs and SVCs as well. The optimization problem is formulated as a mixed-integer quadratic programming problem. To validate the proposed technique, a case study is conducted on a real 41-bus radial feeder in Ontario, Canada using real data for renewable sources, loads, and market prices.

The objective of this study was to assess the effectiveness and practicability of an activity index combining acceleration and location data for automated estrus detection in dairy cows. By using a wearable neck tag, measurements of acceleration and location were gathered from 22 multiparous cows monitored incessantly for 6 days to derive activity records of each cow. The maximum-minimum distance clustering (MMDC) method was used to divide hourly activity data into low, medium, high, and intensity level groups. The weighted sum of the proportions of the low, medium, high, and intensity activities in an hour constituted the activity level. The activity index was defined as the ratio of the variation in hourly activity level compared to the same time period during the previous three days. Furthermore, whether the cow was in estrus was judged above a set threshold. The study showed that the power consumption and communication effects of the neck tags were acceptable for indoor-housing conditions. For the two consecutive time periods, the activity-index-based detection algorithm achieved 90.91% for accuracy, 100% for precision, 100% for specificity, 83.33% for recall, 90.91% for F1 score, and 0.82 for Kappa coefficient. On the basis of these results, it can be concluded that the combination of acceleration and location in the activity index can promote estrus detection in dairy cows.

Bu çalışma belirsizlik problemlerini en iyi şekilde ifade edebilmek ve bu sayede karar verme aşamasında en ideale yakın sonuca ulaşmak için VFP-fuzzy soft kümelerin kullanılmasını önermektedir. Ayrıca bu yeni küme tipinin bazı temel küme işlemleri verilmiş ve bazı özellikleri incelenmiştir. Dahası işlem kalabalığının önüne geçebilecek bazı teknik formülasyonlar verilerek belirsizliğin daha kolay bir şekilde ifade edilebilmesi sağlanmıştır. Son olarak bir karar verme probleminde VFP-soft kümelerden ve FP-fuzzy soft kümelerden daha iyi sonuçlara ulaşıldığı gösterilmiştir.

Many fruit recognition works have applied statistical approaches to make an exact correlation between low-level visual feature information and high-level semantic concepts given by predefined text caption or keywords. Two common fruit recognition models include bag-of-features (BoF) and convolutional neural network (ConvNet), which achieve high-performance results. In most cases, the overfitting problem is unavoidable. This problem makes it difficult to generalize new instances with only a slightly different appearance, although belonging to the same category. This article proposes a new fruit recognition model by associating an object's low-level features in an image with a high-level concept. We define a perceptual color for each fruit species to construct a relationship between fruit color and semantic color name. Furthermore, we develop our model by integrating the perceptual color and semantic template concept to solve the overfitting problem. The semantic template concept as a mapping between the high-level concept and the low-level visual feature is adopted in this model. The experiment was conducted on three different fruit image datasets, with one dataset as train data and the two others as test data. The experimental results demonstrate that the proposed model, called perceptual color on semantic template (PCoST), is significantly better than the BoF and ConvNet models in reducing the overfitting problem.

Currently, in the oil industry, Artificial Lift Optimization (ALO) systems are dealing with different applications including well monitor and control, reservoir management, production optimization, predictive maintenance, artificial lift, and flow assurance, multiphase pumping systems, etc. The scope of this article is to provide a detailed analysis of ALO and predictive pump maintenance applications using Machine Learning (ML) and Artificial Intelligence (AI). The oil and gas industry has experienced a lot of improvements that have impacted the businesses and economies associated with the market in recent times. Issues such as unplanned shutdown time and failure of equipment cause a huge impact on many corporations especially with the current fluctuations in hydrocarbon prices. Similarly, advanced modern technologies such as real-time analysis and predictive maintenance are designed to drive ALO systems. This paper covers several applications and techniques in which ML and AI have been applied to optimize hydrocarbon withdrawal from potentially depleted reservoirs that require some external support to uplift the reservoir fluid from sub surface to surface using artificial lift system. In a nutshell, the applications of AI and ML for the artificial lift selection, their predictive maintenance, equipment malfunctioning detection, etc. using a self-trained system are the main topics of this paper. While reviewing each of these techniques, their workflow is also explained along with the effectiveness of utilizing each application to the current operations.

Extensive reviews and cross-comparison studies are essential to analyze the emerging developments in a specific field of research. In the past decade, hydrocarbon exploration and exploitation from the shale reservoirs have been the most discussed and researched area around the globe. A dramatic development in shale formations became the game-changer, especially in the US. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) are playing an important role in the rapid development in all the industries through automating most of the routine operations. The oil industry is also getting equal benefits of AI and ML for the reservoir development planning and its operational accuracy through a series of automated systems. For the field development, computerized static and dynamic simulation models are generated based on several Petrophysical and Geomechanical properties gathered through different resources that are quite a time taking and expensive. AI and ML have made this process much easier, faster, and economical by means of learning through uncounted experiences from already explored and developed reservoirs, their rock properties, and the cross-ponding fluid flow behavior under different circumstances and hence, predicts accordingly. This article provides a comprehensive literature review in the area of AI and ML application to model Petrophysical and Geomechanical properties using different approaches and algorithms. Also, a systematic publication counts in each field of subject study per year in different literature databases are presented that infect reflects the trending interest in this subject. Finally, multiple AI and ML techniques are discussed in detail that has been tested in the last decade for the sake of achieving more accuracy in Petrophysical and Geo-Mechanical simulation models.

Recently many methods have been induced for plant disease detection by the influence of Deep Neural Networks in Computer Vision. However, the dearth of transparency in these types of research makes their acquisition in the real-world scenario less approving. We propose an architecture named ResTS (Residual Teacher/Student) that can be used as visualization and a classification technique for diagnosis of the plant disease. ResTS is a tertiary adaptation of formerly suggested Teacher/Student architecture. ResTS is grounded on a Convolutional Neural Network (CNN) structure that comprises two classifiers (ResTeacher and ResStudent) and a decoder. This architecture trains both the classifiers in a reciprocal mode and the conveyed representation between ResTeacher and ResStudent is used as a proxy to envision the dominant areas in the image for categorization. The experiments have shown that the proposed structure ResTS (F1 score: 0.991) has surpassed the Teacher/Student architecture (F1 score: 0.972) and can yield finer visualizations of symptoms of the disease. Novel ResTS architecture incorporates the residual connections in all the constituents and it executes batch normalization after each convolution operation which is dissimilar to the formerly proposed Teacher/Student architecture for plant disease diagnosis. Residual connections in ResTS help in preserving the gradients and circumvent the problem of vanishing or exploding gradients. In addition, batch normalization after each convolution operation aids in swift convergence and increased reliability. All test results are attained on the PlantVillage dataset comprising 54 306 images of 14 crop species.

Geochemical parameters are useful properties to enhance hydrocarbon exploration certainty. Though, attaining these parameters, for instance total organic carbon (TOC), volatile and residual hydrocarbon (S1 & S2) has a challenge for geologists due to high cost and time consumption. Therefore, addressing this issue has become an interesting subject for many researchers. As a result, on the ground of conventional well logs, vast kinds of methods, for example, back propagation artificial neural network (BPANN), have been introduced to solve this problem. Implementing these kinds of methods bring scientists tremendous amounts of information related to the richness of organic matter in a meantime. However, the precision of the aforementioned method is inadequate and BPANN is affected negatively by local optimum. Therefore, in the current study to cope with this issue and alleviate the uncertainty, Least Squares Support Vector Machine (LSSVM) and Adaptive-Neuro Fuzzy Inference System (ANFIS) algorithms cooperating with the particle swarm optimization (PSO) were suggested as a suitable method to increase the precision of estimating geochemical factors. The Data bank for this research was attained from available sources of Shahejie formation from Bohai bay basin located in China which consists of geochemical and well logging information. Outputs of this study illustrated that ANFIS-PSO and LSSVM-PSO have a great ability to estimate geochemical parameters. The values of R2 obtained for these two models in order to predict the output parameters of TOC, S1 and S2 are equal to 0.6846 & 0.785, 0.6864 & 0.778, and 0.7343 & 0.8128, respectively. The statistical comparison between these models shows that LSSVM-PSO shows a better performance compared to another model. Also, a new attempt was implemented to evaluate the impacts of input parameters on the outputs and the results of sensitivity analysis suggest that transit interval time had the greatest effect on the output parameters.

The aim of this study was to develop near infrared spectroscopy (NIRS) calibrations to predict quality parameters, dry matter (DM, g kg−1) and crude protein (CP, g kg−1 DM), in fresh un-dried grass. Knowledge of these parameters would enable more precise allocation of quality herbage to grazing livestock. Perennial ryegrass samples (n = 1 615) were collected over the 2017 and 2018 grazing seasons at Teagasc Moorepark to develop a NIRS calibration dataset. Additional samples were collected for an independent validation dataset (n = 197) during the 2019 grazing season. Samples were scanned using a FOSS 6500 spectrometer at 2 nm intervals in the range of 1 100 ~ 2 500 nm and absorption was recorded as log 1/Reflectance. Reference wet chemistry analysis was carried out for both parameters and the resultant data were calibrated against spectral data by means of modified partial least squares regression. A range of mathematical spectral treatments were examined for each calibration, which were ranked in order of standard error of prediction (SEP) and ratio of percent deviation (RPD). Best performing calibrations achieved high predictive precision for DM (R2 = 0.86 SEP = 9.46 g kg−1, RPD = 2.60) and moderate precision for CP (R2 = 0.84 SEP = 20.38 g kg−1 DM, RPD = 2.37). These calibrations will aid the optimisation of grassland management and the development of precision agricultural technologies.

The Acacia longifolia species is known for its rapid growth and dissemination, causing loss of biodiversity in the affected areas. In order to avoid the uncontrolled spread of this species, it is important to effectively monitor its distribution on the agroforestry regions. For this purpose, this paper proposes the use of Convolutional Neural Networks (CNN) for the detection of Acacia longifolia, from images acquired by an unmanned aerial vehicle. Two models based on the same CNN architecture were elaborated. One classifies image patches into one of nine possible classes, which are later converted into a binary model; this model presented an accuracy of 98.6 % and 98.5 % in the validation and training sets, respectively. The second model was trained directly for binary classification and showed an accuracy of 98.8 % and 98.7 % for the validation and test sets, respectively. The results show that the use of multiple classes, useful to provide the aerial vehicle with richer semantic information regarding the environment, does not hamper the accuracy of Acacia longifolia detection in the classifier’s primary task. The presented system also includes a method for increasing classification’s accuracy by consulting an expert to review the model’s predictions on an automatically selected sub-set of the samples.

Long horizon state of health (SOH) monitoring and remaining useful life (RUL) prediction are of industrial value in prognostic and health management (PHM) of lithium-ion batteries (LIBs) to ensure their reliable functionality by early detection. Machine Learning, as a data-driven health diagnostic technique, has been widely utilized in solitary and hybrid structures. However, an accurate SOH estimation and RUL prediction method with less computational burden are highly desirable for the online state prediction in an electric vehicle application. This paper evaluates nonlinear autoregressive with external input (NARX) recurrent neural network (RNN) and time delay neural network (TDNN) in their prediction precision using the NASA dataset. The superior method, NARXRNN, is employed for two different datasets to estimate the battery's SOH and predict its RUL on a broad horizon. The results reveal the outstanding performance by presenting the root mean square error within 3% and mean absolute error within 2% for unseen data. Therefore, this method is capable to accurately predict the SOH of LIBS from historical data at low computational complexity. It is a promising model for long horizon SOH and RUL prediction and practical for online applications.

Bu çalışma özellikle bilim ve mühendislik alanlarında karşılaşılabilen belirsizlik problemlerinin uygulama alanını genişletebilmek için iki önemli teori olan nötrosofi ve esnek kümelere odaklanmaktadır. Bunun için sanal nötrosofik parametreli esnek küme teorisi tanımlanarak önemli bazı özellikleri verilmiştir. Daha sonra, belirsizliğin ideal çözüme yaklaştırılmasında sanal nötrosofik parametreli esnek küme teorisinin nötrosofik parametreli esnek küme teorisinden daha başarılı olduğu bir algoritma yardımıyla gösterilerek benzeri problemlerin çözümü için sanal nötrosofik parametreli esnek kümelerin kullanılması önerilmiştir. Ayrıca çalışmadaki özel parametre kümeleri, belirsizlik problemlerinin çözümünde daha fazla alternatif çözüm yolunu mevcut kılmaktadır. Bu sayede birçok çözüm yolundan ideale en yakın olanı seçmeyi kolaylaştırmaktadır.

Methane hydrate has become one promising energy resource for humankind. In this study, recent worldwide onshore/offshore drilling and production activities are analysed. The most recent reports from sites such as Japan (2013 and 2017) and China (2017) show that the road towards comprehensive utilisation of methane hydrate is underway. Though the production methods (depressurisation, thermal stimulation and carbon dioxide (CO2) replacement) diverge in safety and efficiency, potential has been confirmed for future large-scale utilisation. The world hydrate policy is dependent on the socioeconomic status and environmental concerns in major countries. It is urged that international regulations on commercial explorations be settled for methane hydrate projects.

This paper presents a survey of image processing techniques proposed in the literature for extracting key cereal crop growth metrics from high spatial resolution, typically proximal images. The descriptive crop growth metrics considered are: crop canopy cover, above ground biomass, leaf area index (including green area index), chlorophyll content, and growth stage. The paper includes an overview of relevant fundamental image processing techniques including camera types, colour spaces, colour indexes, and image segmentation. The descriptive crop growth metrics are defined. Reference methods for ground-truth measurement are described. Image processing methods for metric estimation are described in detail. The performance of the methods is reviewed and compared. The survey reveals limitations in image processing techniques for cereal crop monitoring such as lack of robustness to lighting conditions, camera position, and self-obstruction. Directions for future research to improve performance are identified.

In this paper, two novel approaches for identifying the synchronous generator (SG) parameters are presented. The proposed approaches use an experimentally obtained armature current during the short-circuit test as input data. Both approaches are simple, cost-effective, and practical since they do not require any additional equipment for signal generation nor applying any of the advanced control techniques. The first approach relies on fitting the analytical total armature current waveform with the experimental one, while the second approach relies on matching the observed waveform with an analytical waveform of asymmetrical and alternating components. Instead of traditional graphical methods typically used in standardized procedures, this paper also proposes a novel hybrid metaheuristic algorithm to identify the SGs parameters. The experimental verification of the proposed methods is carried out on 100 MVA SG at the Bajina Basta hydropower plant (HPP) in Serbia.

Determination of an appropriate location for monitoring soil water content (SWC) is a key factor in efficient use of water in precision agriculture, however, the main challenge is the dynamic movement of water and root development in the soil profile. The objective of this study was investigating how SWC distribution in a loam soil profile at two growth stages of maize may impact the suitable location for SWC monitoring in a drip-tape irrigation system. A new concept, Average Moisture Representative Surface (AMRS) was proposed to determine the surface of the soil profile, which represent the average soil moisture of the wetted volume. SWC samples were taken during two irrigation intervals (48~52 days after planting (DAP) and 68~73 DAP) and root growth pattern was studied through root length density (RLD) at 50 and 100 DAP. The results revealed that a non-uniform wetting pattern after irrigation limits the appropriate locations for SWC monitoring to point measurements and with time, SWC depletion resulted in enlarging AMRS. At the end of growing season, an increase of root growth around the drippers increased the variation of root water uptake in different soil layers, and thus optimal place for soil sensors was limited to the upper layers, where the maximum root water uptake occurred. Overall, it is recommended to install soil sensors, such as tensiometers and TDRs at a horizontal distance of 5~20 cm from the crop and a depth of 10~20 cm from the soil surface while drip-tape is aligned close to the maize row.

Plant factories have a great potential for mitigating the contradiction between the world’s growing population and food scarcity. During the process of its automatic production, machine vision plays a significant role. This technique almost covers every production link from raising seedlings, transplanting, management, and harvesting to fruit grading. To provide references and a starting point for those who are committed to studying this issue. In this paper, the application prospects of machine vision in plant factories were analyzed, and the present researches were summarized from the fields of plant growth monitoring, robot operation assistance, and fruit grading. The results found that although the existing methods have solved some practical problems at low cost, high efficiency and precision, some challenges still are faced by machine vision. Firstly, the changing lighting, complex backgrounds, and color similarity within plant different parts cause the commonly used image segmentation algorithms to fail. The shortage of standard agricultural datasets also keeps deep learning and unsupervised classification algorithms from making progress. Secondly, there are some theoretical knowledge gaps for machine vision application in a particular environment of plant factories, which seriously contains its application effect. Thirdly, the lack of special image acquisition devices and supporting facilities resulted in poor image quality. All these factors hinder machine vision application in plant factories. Nevertheless, it is still a powerful tool and irreplaceable at present. We believed that this technique would promote plant factory development greatly with more robust, efficient, and reliable algorithms are developed in the future.

Artificial Intelligence (AI) is becoming popular for the Rate of Penetration (ROP) estimation, hence, the need to study the best techniques and their advantages over empirical models. Various literatures were analysed to determine the prevalence of AI in ROP computation and compare the computation accuracies with empirical models. Artificial Neural Network (ANN) accounted for over 92% of the AI techniques used for ROP computation and Weight on Bit (WOB) mostly influenced the computation accuracy. The accuracy of AI algorithms are better than the empirical models thus, will improve the drilling efficiency, reduce cost and enhance the development of pad wells.

The continuous evaluation of the measured Stand Pipe Pressure (SPP) against a modeled SPP value in real-time involves the automatic detection of undesirable drilling events such as drill string washouts and mud pump failures. Numerous theoretical and experimental studies have been established to calculate the friction pressure losses using different rheological models and based on an extension of pipe flow correlations to an annular geometry. However, it would not be feasible to employ these models for real-time applications since they are limited to some conditions and intervals of application and require input parameters that might not be available in real-time on each rig. In this study, we applied the Group Method of Data Handling (GMDH) to develop a trustworthy model that can predict the SPP in real-time as a function of mud flow, well depth, RPM and the Fan VG viscometer reading at 600 and 300 rpm. In order to accomplish the modeling task, 3351 data points were collected from two wells from Algerian fields. Graphical and statistical assessment criteria disclosed that the model predictions are in excellent agreement with the experimental data with a coefficient of determination of 0.9666 and an average percent relative error less than 2.401%. Furthermore, another data (1594 data points) from well-3 was employed to validate the developed correlation for SPP. The obtained results confirmed that the proposed GMDH-SPP model can be applied in real-time to estimate the SPP with high accuracy. Besides, it was found that the proposed GMDH correlation follows the physically expected trends with respect to the employed input parameters. Lastly, the findings of this study can help for the early detection of downhole problems such as drill string washout, pump failure, and bit balling.

This paper introduces a novel frequency domain technique to globally evaluate the accuracy of electro-magnetic transient simulations. It is shown that simulation accuracy at low frequencies can sometimes be poorer than at high frequency. A modified approach which quantifies accuracy from a driving point as a function of frequency is also introduced that uses the Bilinear Transformation and Norton equivalents, to produce a simulation accuracy spectrum. This approach can be applied to large systems without explicitly forming state space equations. It also permits the accuracy analysis of networks with distributed components such as frequency dependent transmission lines. Two examples are used to verify the proposed technique, a small network with a frequency dependent transmission line modeled by the universal line model; and the IEEE 39 bus system connected with an LCC-HVdc system.

Early detection of infected asymptomatic plants could prevent the dissemination of tomato bacterial canker throughout the crop and, thus, reduce associated economic losses. Reflectance spectroscopy has been proposed for the detection of diseases in plants, mainly caused by foliar fungal pathogens. In contrast, bacterial canker is a vascular disease that does not produce visible foliar symptoms until it is too late to avoid contagion to other plants. The effects of the disease on the conductivity of the xylem have an impact on the water content of the leaves; initially, a subtle effect that cannot be assessed with the naked eye but can be evaluated by reflectance spectroscopy. The objective of this work was to identify indices, based on the reflectance spectral signature of the plants, for the detection of tomato plants affected by bacterial canker prior to the appearance of symptoms. To verify that the proposed methodology is applicable to a broad range of tomato genotypes, two experiments were carried out with different tomato cultivars grown in pots and another on the ground in a greenhouse. Plants were inoculated with the pathogenic bacteria and the reflectance spectrum of the apical leaflet of two leaves per plant were obtained before symptoms expression; several spectral indices were calculated from the reflectance spectra. Three indices, of the shortwave-infrared zone, allowed the detection of bacterial canker inoculated plants in a fast and non-destructive way, up to one week before symptoms were visible: Normalized Difference Water Index (NDWI), Simple Ratio of Water Index (SRWI) and Water Index 1 180 (WI1180). Our research demonstrates, for the first time, the usefulness of spectral indices sensitive to water content for the identification of diseased plants. These indices arise as a promising tool for the early detection of vascular plant diseases, that could be integrated into sustainable management plans.

In the process of shale gas drilling, geo-steering plays an important role in shale gas drilling. This paper analyzes the constituent elements of shale formation, and selects the most suitable constituent elements of shale formation. A particle swarm optimization algorithm based on improved inertia weight and acceleration factor is proposed to optimize the parameters of support vector machine. The lithology identification model of shale formation is established based on IPSO-SVM. According to the experimental analysis based on the field historical data, the recognition rate of IPSO-SVM is increased by 17.79%, 10.17% and 8.05%, respectively, compared with SVM, GA-PSO and PSO-SVM. In terms of running time, the running time of IPSO-SVM is 13.76s and 9.5s shorter than that of GA-PSO, PSO-SVM, respectively. By comparing the experimental results of different models, IPSO-SVM has the advantages of strong robustness, strong reliability, high accuracy and fast convergence speed. It provides a theoretical basis for precise geo-steering and finding the optimal shale layer.

Lodging occurs when the crop canopy is too heavy for the strength of the stem and it falls over onto the ground. This decreases crop yield and quality, and it makes harvest difficult. A research experiment was set up in a spearmint field on a center pivot with mid elevation spray application (MESA) overhead sprinklers, where the water was applied from a “mid elevation” of 2 m above the ground level (AGL), and low elevation precision application (LEPA) sprinklers, where the water was emitted directly onto the soil surface through drag hoses without wetting the crop canopy. Every-other span of this full-size center pivot was configured with MESA and LEPA sprinklers alternatively. In 2018, imagery was collected with an unmanned aerial vehicle (UAV) from a cross section of this field. In 2019, a cross section was again collected, but in addition UAV imagery was collected from marked lodged and un-lodged areas of the field to validate the lodging detection method. These UAV-based imagery data were captured with a ground sample distance (GSD) of 0.03 m. This research introduces using the texture feature, which is based on image entropy, was used to evaluate the degree of lodging. The results from 2018 showed that the average entropy of the grayscale image from LEPA (5.5 (mean) ± 0.27 (standard deviation)) was significantly (P < 0.0001) greater than the average entropy (5.0 ± 0.25) of MESA. Also, the entropy value extracted from the images in 2019 from the marked un-lodged locations were significantly higher compared to that of the lodged areas. Overall, the LEPA irrigation treatment was significantly less lodged compared to MESA. Moreover, the entropy value, or texture feature, is a viable method for estimating lodging using low altitude RGB imagery.

In this paper, the influence of the variation in transformer oil temperature on the accuracy of the all-acoustic non-iterative method for partial discharge location in a power transformer is researched. The research can improve power transformers’ testing and monitoring, particularly given the large transformer oil temperature variations during real-time monitoring. The research is based on quantifying the contribution of oil temperature to the standard combined measurement uncertainty of the non-iterative algorithm by using analytical, statistical, and Monte Carlo methods. The contribution can be quantified and controlled. The contribution varied significantly with different mutual placements of partial discharge and acoustic sensors. The correlation between the contribution and the mean distance between partial discharge and acoustic sensors was observed. Based on these findings, the procedure to quantify and control the contribution in practice was proposed. The procedure considers the specificity of the method’s mathematical model (the assumption that the oil temperature is constant), the non-iterative algorithm’s nonlinearity, and the large variations in transformer oil temperature. Existing studies did not consider the significant effect of the oil temperature on the combined measurement uncertainty of partial discharge location influenced by those phenomena. The research is limited to partial discharge located in the transformer oil.

In the last decades, several studies have revealed how critical the urban heat island (UHI) effect can be in cities located in cold climates, such as the Canadian one. Meanwhile, many researchers have looked at the impact of the city design over the urban microclimate, and have raised concerns about the development of too dense cities. Under the effect of the “Places to Growth” plan, the city of Toronto is experiencing one of the highest rates of building development in North America. Over 48,000 and 33,000 new home permits were issued in 2012 and 2013 respectively, and at the beginning of 2015, almost 500 high-rise proposals across the Greater Toronto Area were released. In this context, it is important to investigate how new constructions will affect the urban microclimate, and to propose strategies to mitigate possible UHI effects. Using the software ENVI-met, microclimate simulations for the Church-Yonge corridor both in the current situation and with the new constructions are reported in this paper. The outdoor air temperature and the wind speed are the parameters used to assess the outdoor microclimate changes. The results show that the new constructions could increase the wind speed around the buildings. However, high-rise buildings will somewhat reduce the air temperature during day-time, as they will create large shadow areas, with lower average mean radiant temperature.