This article presents a model to estimate the energy use and fuel consumption of cruise ships that sail Norwegian waters. Automatic identification system (AIS) data and technical information about cruise ships provided input to the model, including service speed, total power, and number of engines. The model was tested against real-world data obtained from a small cruise vessel and both a medium and large cruise ship. It is sensitive to speed and the corresponding engine load profile of the ship. A crucial determinate for total fuel consumption is also associated with hotel functions, which can make a large contribution to the overall energy use of cruise ships. Real-world data fits the model best when ship speed is 70⁻75% of service speed. With decreased or increased speed, the model tends to diverge from real-world observations. The model gives a proxy for calculation of fuel consumption associated with cruise ships that sail to Norwegian waters and can be used to estimate greenhouse... [more]

It is well known that accurate identification of the key state parameters and State of Charge (SOC) estimation method for a Li-ion battery cell is of great significance for advanced battery management system (BMS) of electric vehicles (EVs), which further facilitates the commercialization of EVs. This study proposed a systematic experimental data-driven parameter identification scheme and an adaptive extended Kalman Filter (AEKF)-based SOC estimation algorithm for a Li-Ion battery equivalent circuit model in EV applications. The key state parameters of Li-ion battery cell were identified based on the second-order resistor capacitor (RC) equivalent circuit model and the experimental battery test data using genetic algorithm (GA). Meanwhile, the proposed parameter identification procedure was validated by carrying out a comparative study of the simulated and experimental output voltage under the same input current profile. Then, SOC estimation was performed based on the AEKF algorithm. F... [more]

Recent discoveries of oil and gas have principally been located in the central part of the Ordos Basin, which is a petroliferous basin with the largest discovered reserves and annual production of tight sandstone gas in China. For tight sandstone gas reservoirs in the transition zone of the basin margin, the process of natural gas accumulation has remained relatively vaguely understood, because of the transitional accumulation of geological conditions such as structure, sedimentation, and preservation. In this study, thin-section identification and scanning electron microscopic observations of the reservoir core, measurement of the physical properties of the reservoir, microscopic petrography research and measurement of the homogenization temperature of fluid inclusions, digital simulations, and laser Raman spectroscopy analysis were combined to analyze the process of natural gas accumulation of the Permian Lower Shihezi Formation in Duguijiahan block, Hangjinqi area, northern Ordos Ba... [more]

For geological analysis tasks such as reservoir characterization and petroleum exploration, lithology identification is a crucial and foundational task. The logging lithology identification tasks at this stage generally build a lithology identification model, assuming that the logging data share an independent and identical distribution. This assumption, however, does not hold among various wells due to the variations in depositional conditions, logging apparatus, etc. In addition, the current lithology identification model does not fully integrate the geological knowledge, meaning that the model is not geologically reliable and easy to interpret. Therefore, we propose a cross-domain lithology identification method that incorporates geological information and domain adaptation. This method consists of designing a named UAFN structure to better extract the semantic (depth) features of logging curves, introducing geological information via wavelet transform to improve the model’s interpr... [more]

Inverters are playing an increasingly important role in the electrical utility grid due to the proliferation of renewable energy sources. Obtaining inverter models with accurate parameters is, therefore, essential for grid studies and design. In this paper, a methodology to estimate the output impedance and parameters of a residential grid-tied inverter is proposed. The methodology is first verified through simulation. A sensitivity analysis is conducted to determine the influence of the filter and controller parameters on the output impedance of the inverter. The simulated output impedance, voltage, and current are used in a parameter estimation methodology to obtain filter and controller parameters. It is shown that up to seven parameters can be estimated accurately. The proposed methodology is further investigated through a practical experiment. Two perturbation sources, the pseudo-random binary sequence perturbation and pseudo-random impulse sequence perturbation, are used, in turn... [more]

Natural gas power generation has the advantages of flexible operation, short start−stop times, and fast ramp rates. It has a strong peaking capacity and speed compared to coal power generation, and can greatly reduce emissions of harmful substances such as sulphur dioxide. However, in practice, the accurate identification of borehole fluvial facies in the exploration area is one of the most important conditions affecting the success of gas field exploration. An insufficient number of drilling points in the exploration area and the accurate identification of lithological data features are key to the correct identification of borehole fluvial facies, and understanding how to achieve accurate identification of borehole fluvial facies when there are insufficient training data is the focus and challenge of research within the field of natural gas energy exploration. This paper proposes a borehole fluvial facies identification method applicable to the sparse sample size of drilling points, u... [more]

Variations in generator parameters that occur during the operation of a doubly-fed induction wind turbine (DFIG) constitute a significant factor in the degradation of control performance. To address the problem of difficulty in identifying multiple parameters simultaneously in DFIG, a parameter identification method depending on the adaptive grey wolf algorithm with an information-sharing search strategy (ISIAGWO) is proposed to solve the problem of low accuracy and slow identification speed of multiple parameters in DFIG. The easily obtainable generator outlet current was selected as the observed quantity, and the trajectory sensitivity analysis was performed on the five electrical parameters of the DFIG to derive its discriminability. Finally, the parameter recognition of the DFIG was carried out using the ISIAGWO algorithm in the MATLAB/Simulink simulation software, applying the proposed calculation functions. The experimental results show that the ISIAGWO algorithm has better ident... [more]

It is of great significance to obtain the source of mixed gas emission from the working face and the law of gas emission from each coal seam for the targeted implementation of gas control measures. Based on the principle that the hydrocarbon isotope values of gas in different coal seams have significant variability, a hydrocarbon isotope method for identifying the source of gas emission is proposed. Taking Pingmei No. 6 Coal Mine as the study area, the distribution characteristics of each value were obtained by testing the values of carbon and hydrogen isotopes in the gas of mined coal seams and adjacent coal seams; by testing the hydrocarbon isotope value of CH4 in the mixed gas of coal seam, the proportion of gas emission in each coal seam is determined and the law of gas emission in each coal seam is studied. The results show that the variation law of the proportion of gas emission in each coal seam can be divided into three stages: the dominant stage of gas emission in the mining l... [more]

This paper aims to address the difficult to pinpoint fault cause of the full parallel AT traction power supply system with special structure. The fault characteristics are easily covered up, and high transition impedance only affects the singularity of the wavehead, making the traveling waves hard to identify. Moreover, the classification accuracy of the traditional time-frequency analysis method is not sufficiently high to distinguish precisely. In this paper, a fault classification method of traction network based on single-channel improved Hilbert−Huang transform and deep learning is proposed. This method extracts effective fault features directly from the original fault signals and classifies the fault types at the same time. The accuracy of data categorization is increased by directly applying the Hilbert−Huang transform to fault signals to extract transient fault features and produce one-dimensional feature data, which are analyzed by the time-frequency energy spectrum. Using the... [more]

The rapid development of natural gas pipelines has highlighted the need to utilize SCADA (supervisory control and data acquisition) system data. In this paper, a heat transfer model of a natural gas pipeline based on data feature extraction and first principle models, which makes full use of the measured temperatures at each end of the pipeline, is proposed. Three methods, the NARX neural network (nonlinear autoregressive neural network with exogenous inputs), time series decomposition, and system identification, were used to model the changes of gas temperatures of the pipeline. The NARX neural network method uses a cyclic neural network to directly model the relationship of temperature between the start and the end of the pipeline. The measured temperature series at the pipeline inlet and outlet were decomposed into trend items, fluctuation items, and noise items based on the time series decomposition method. Then the three items were fitted separately and combined to form a new temp... [more]

Interest has grown regarding natural plant extracts in food and beverage applications, their vital role in food quality and technology, and their therapeutic use in inhibiting several diseases [...]

Linear system identification is a well-known methodology for building mathematical models of dynamic systems from observed input−output data. It also represents an essential tool for model-based control design, adaptive control and other advanced control techniques. Use of linear identification is, however, often limited to academic environment and to research facilities equipped with scientific computing platforms and highly qualified staff. Common industrial or building control system technology rarely uses these advanced design techniques. The main obstacle is typically lack of experience with their practical implementation. In this article, a procedure is proposed, implemented, and tested, that brings the benefits of linear identification into broader control system practice. The open-source DCU control system platform with its advanced control framework is used for implementation of the proposed linear identification procedure. The procedure is experimentally tested in the laborat... [more]

In the recent past, the world in general and Pakistan in particular faced a drastic fuel price change, affecting the economic productivity of the country. This has drawn the attention of empirical researchers to analyze the abrupt change in fuel prices. This study takes a lead and investigates for the first time, in the literature related to Pakistan, the presence of multiple fuel price bubbles, with the purpose of knowing if the price driver is due to demand or it is exuberant consumer behavior that prevails and contributes to a sudden boom in fuel price series. The empirical analysis is performed through a recently proposed state-of-the-art generalized sup ADF (GSADF) approach on six commonly used fuel price series, namely, LDO (light diesel oil), HSD (high-speed diesel), petrol, natural gas, kerosene, and MS (motor spirit). The bubble analysis for each of the six fuel price series is based on monthly data from July 2005 to August 2020. The findings provide evidence of the existence... [more]

Polycystic ovarian syndrome (PCOS) is an endocrine disorder that is characterized by hyperandrogenism. Therefore, information about androgen-induced molecular changes can be obtained using the tissues of patients with PCOS. We analyzed two microarray datasets of normal and PCOS muscle samples (GSE8157 and GSE6798) to identify androgen-responsive genes (ARGs). Differentially expressed genes were determined using the t-test and a meta-analysis of the datasets. The overlap between significant results of the meta-analysis and ARGs predicted from an external database was determined, and differential coexpression analysis was then applied between these genes and the other genes. We found 313 significant genes in the meta-analysis using the Benjamini−Hochberg multiple testing correction. Of these genes, 61 were in the list of predicted ARGs. When the differential coexpression between these 61 genes and 13,545 genes filtered by variance was analyzed, 540 significant gene pairs were obtained us... [more]

Chemical and oil processes are intrinsically sources of potential hazards. Although traditional qualitative hazard identification methods are simple, systematic, and flexible, such methodologies present limitations related to the inherent subjectivity, dependence on the team’s level of experience, and widespread time consumption of the members involved. In this context, the present work aims to develop a systematic way to use computational modeling and simulation tools for hazard identification. After extensive literature review, the present work proposes a methodology based on the association of the main points of previous works, with new contributions regarding the preparation for the simulations and the characterization of the minimum set of process variables that can enable appropriate interpretation of the results. The propene polymerization process (LIPP-SHAC process) was used as a case study to illustrate the proposed procedure. The paper explores how the model can be adapted fo... [more]

The accuracy of doubly fed induction generator (DFIG) models and parameters plays an important role in power system operation. This paper proposes a parameter identification method based on the hybrid genetic algorithm for the control system of DFIG converters. In the improved genetic algorithm, the generation gap value and immune strategy are adopted, and a strategy of “individual identification, elite retention, and overall identification” is proposed. The DFIG operation data information used for parameter identification considers the loss of rotor current, stator current, grid-side voltage, stator voltage, and rotor voltage. The operating data of a wind farm in Zhangjiakou, North China, were used as a test case to verify the effectiveness of the proposed parameter identification method for the Maximum Power Point Tracking (MPPT), constant speed, and constant power operation conditions of the wind turbine.

A system identification of a two-wheeled robot (TWR) using a data-driven approach from its fundamental nonlinear kinematics is investigated. The fundamental model of the TWR is implemented in a Simulink environment and tested at various input/output operating conditions. The testing outcome of TWR’s fundamental dynamics generated 12 datasets. These datasets are used for system identification using simple autoregressive exogenous (ARX) and non-linear auto-regressive exogenous (NLARX) models. Initially the ARX structure is heuristically selected and estimated through a single operating condition. We conclude that the single ARX model does not satisfy TWR dynamics for all datasets in term of fitness. However, NLARX fitted the 12 estimated datasets and 2 validation datasets using sigmoid nonlinearity. The obtained results are compared with TWR’s fundamental dynamics and predicted outputs of the NLARX showed more than 98% accuracy at various operating conditions.

The mechanical response of a tendon to load is strictly related to its complex and highly organized hierarchical structure, which ranges from the nano- to macroscale. In a broader context, the mechanical properties of tendons during tensile tests are affected by several distinct factors, due in part to tendon nature (anatomical site, age, training, injury, etc.) but also depending on the experimental setup and settings. This work aimed to present a systematic review of the mechanical properties of tendons reported in the scientific literature by considering different anatomical regions in humans and several animal species (horse, cow, swine, sheep, rabbit, dog, rat, mouse, and foal). This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. The literature research was conducted via Google Scholar, PubMed, PicoPolito (Politecnico di Torino’s online catalogue), and Science Direct. Sixty studies were selected and analyzed... [more]

Person re-identification(Re-ID) technology has been a research hotspot in intelligent video surveillance, which accurately retrieves specific pedestrians from massive video data. Most research focuses on the short-term scenarios of person Re-ID to deal with general problems, such as occlusion, illumination change, and view variance. The appearance change or similar appearance problem in the long-term scenarios has has not been the focus of past research. This paper proposes a novel Re-ID framework consisting of a two-branch model to fuse the appearance and gait feature to overcome covariate changes. Firstly, we extract the appearance features from a video sequence by ResNet50 and leverage average pooling to aggregate the features. Secondly, we design an improved gait representation to obtain a person’s motion information and exclude the effects of external covariates. Specifically, we accumulate the difference between silhouettes to form an active energy image (AEI) and then mask the m... [more]

This paper presents a newly created mathematical model of thermophilic anaerobic digestion of wheat straw carried out in a 2 dm3 bioreactor for methane production. Two batch processes, with 30 mL/dm3 and 35 mL/dm3 organic load, are carried out—one set for parameter identification and one set for model verification. The identification of model parameter values is based on dynamical experiments. It is fulfilled using two different techniques: deterministic sequential quadratic programming algorithm and metaheuristic genetic algorithm. Verification of the developed mathematical models is conducted based on the different data sets of the process. Both models predict the set of the experimental data for all considered process variables well. Genetic algorithm visually fits the data with a higher degree of accuracy, as confirmed by the numerical results for the objective function value.

With the increasing demand for security and privacy, identity recognition based on the unique biometric features of ECG signals is gaining more and more attention. This paper proposes a feature reuse residual network (FRRNet) model to address the problem that the recognition accuracy of conventional ECG identification methods decreases with the increase in the number of testing samples at different moments or in different heartbeat cycles. The residual module of the proposed FRRNet model uses the adding layers of max pooling (MP) and average pooling (AP), and the proposed model splices the deep network with the shallow network to reduce noise extraction and enhance feature reuse. The FRRNet model is tested on 20 and 47 subjects under the MIT-BIH dataset, and its recognition accuracy is 99.32% and 100%, respectively. Additionally, the FRRNet model is tested on 50 and 87 subjects under the PhysioNet/Computing in Cardiology Challenge 2017 (CinC_2017) dataset, and its recognition accuracy... [more]

This article presents a cloud-based system for the on-line monitoring of tool conditions in end milling. The novelty of this research is the developed system that connects the IoT (Internet of Things) platform for the monitoring of tool conditions in the cloud to the machine tool and optical system for the detection of cutting chip size. The optical system takes care of the acquisition and transfer of signals regarding chip size to the IoT application, where they are used as an indicator for the determination of tool conditions. In addition, the novelty of the presented approach is in the artificial intelligence integrated into the platform, which monitors a tool’s condition through identification of the current cutting force trend and protects the tool against excessive loading by correcting process parameters. The practical significance of the research is that it is a new system for fast tool condition monitoring, which ensures savings, reduces investment costs due to the use of a mo... [more]

The principle about the nonlinear ultrasonic mixed frequency is introduced. A novel identification method for incipient structural health prognosis is proposed based on heterolateral co-linear mixed-frequency ultrasound to identify the micro-crack in mechanical structures. The modelling analysis methodology by the application of finite element analysis (FEM) is developed to simulate the nonlinear mixed-frequency ultrasonic wave transmission mechanism from the cracks with different depths and the excited frequency. The correlation models between the crack widths and the mixed-frequency nonlinear coefficients are established. An experimental method based on the nonlinear mixed-frequency ultrasonic theory is proposed to actuate the differential and sum-frequency characteristic mixed waves that interact with the defects of materials, which obtains the nonlinear coefficients to identify the depths of cracks in materials. The FEM model is verified to be effective at predicting the width of t... [more]

To solve the problems of high labor intensity, low efficiency, and frequent errors in the manual identification of cone yarn types, in this study five kinds of cone yarn were taken as the research objects, and an identification method for cone yarn based on the improved Faster R-CNN model was proposed. In total, 2750 images were collected of cone yarn samples in real of textile industry environments, then data enhancement was performed after marking the targets. The ResNet50 model with strong representation ability was used as the feature network to replace the VGG16 backbone network in the original Faster R-CNN model to extract the features of the cone yarn dataset. Training was performed with a stochastic gradient descent approach to obtain an optimally weighted file to predict the categories of cone yarn. Using the same training samples and environmental settings, we compared the method proposed in this paper with two mainstream target detection algorithms, YOLOv3 + DarkNet-53 and F... [more]

Quality changes of protein and non-protein nitrogen compounds during industrial fishmeal processing of fatty pelagic species (mackerel/herring rest material blend, MHB) and lean fish (whole blue whiting, BW) were studied to identify processing steps that require optimization to allow production of products for human consumption. Samples from protein-rich processing streams throughout the fishmeal production were analyzed for proximate composition, salt soluble protein content (SSP), biogenic amines (BA), total volatile basic nitrogen (TVB-N), trimethylamine (TMA), and dimethylamine (DMA). Mass flows throughout processing were balanced based on the total mass and proximate composition data. The quality of the final fishmeal products was highly dependent on the fish species being processed, indicating that the processes require optimization towards each raw material. The chemical composition changed in each processing step, resulting in different properties in each stream. Most of the no... [more]