International Journal of Engineering
Volume:36 Issue: 8, Aug 2023

Integration of electric vehicles (EVs) into the power systems has been a concern for distribution system operators due to their impacts on several aspects of power system operation, such as congestion management, power quality, voltage regulation, and peak time changing. In this paper uncertainty parameters such as charging time, traveled distance, and plug-in location of EVs are considered and their effects on the optimal daily operation of microgrids (MG) are discussed. A power system, including geographically-adjacent quasi-independently controlled MGs, each of which has a different operation objective function (OF) is modeled in this paper. A set of socioeconomic OFs i.e. minimum purchase power from the main grid, maximum usage of green power, and minimum Expected Energy Not Supplied (EENS) are considered for each MG which appear in the optimization process with different weights based on the MG policy. The effect of EV integration into the Multi Microgrid System (MMS) is also investigated in this paper and the performance effectiveness of different operation management policies against EV integration is discussed.

Over 40% of the world's energy consumption occurs in the construction sector. However, some countries do not address environmental criteria as design requirements in their construction codes. Accordingly, this research aims to provide a solution that reduces embodied energy and carbon while preserving historical and traditional textures of Iran. The comparison of embodied carbon and energy between new concrete and traditional buildings was performed by calculating the amount of construction materials. By examining both types of buildings, the reduction of embodied carbon and energy in a combined building system was evaluated. In the following, using SWOT analysis, the strategies of this combination were investigated. Clay building has less embodied energy and carbon than concrete one despite containing more mass of materials. According to SWOT analysis, the strategy of integrating clay and concrete systems is presented. The proposed system in compare to the concrete structure resulted in around 40% and 35% reduction in embodied carbon and energy, respectively. Extending this strategy throughout the country saves 13 million tons of embodied carbon and 130 million GJ of embodied energy. Finding a solution based on sustainability considerations to preserve historical texture is one of the basic concerns of countries where these textures form a part of their identity. The presented combined system, while paying attention to sustainable building and urban development, is a desirable solution to reduce buildings' embodied carbon and energy.

Many problems do not have one or more variables that determine quality characteristics. In these situations, as a solution method, a profile is descibed by linking independent variables to the response variable. One of the common assumptions in most monitoring schemes is the assumption of independent residuals. Contravention of this assumption can lead to misleading results of the control chart. On the other hand, when the data are contaminated, the classical methods of estimating the parameters do not perform well. Such situations require robust estimation methods. Hence, this paper proposes a robust method to estimate the process parameters for Phase I monitoring autocorrelated multiple linear profiles. The developed control chart is appraised in the absence and presence of contaminated data through comprehensive simulation studies. The results showed that the robust estimator decreases the impact of contaminated data on the performance of the proposed control chart for all outlier percentages and shift magnitudes. Generally, in all three scenarios, including outliers in the model parameters and error variance, the robust approach performs better than the comparative method.

The utilization of artificial intelligence and computer vision has been extensively explored in the context of human activity and behavior recognition. Numerous researchers have investigated and suggested various techniques for human action recognition (HAR) to accurately identify actions from real-time videos. Among these techniques, convolutional neural networks (CNNs) have emerged as the most effective and widely used for activity recognition. This work primarily focuses on the significance of spatial information in activity/action classification. To identify human actions and behaviors from large video datasets, this paper proposes a two-stream spatial CNN approach. One stream, based on RGB data, is fed with the spatial information from unprocessed RGB frames. The second stream is powered by graph-based visual saliency maps generated by GBVS (Graph-Based Visual Saliency) method. The outputs of the two spatial streams were combined using sum, max, average, and product feature fusion techniques. The proposed method is evaluated on well-known benchmark human action datasets, such as KTH, UCF101, HMDB51, NTU RGB-D, and G3D, to assess its performance Promising recognition rates were observed on all datasets.

In the strut-and-tie (STM) method of design, the internal mechanism of flow of forces is represented by hypothetical truss in which the behavior of the beam is controlled by the strut connecting load and support points. The strength of such strut is correlated to the shear capacity of the deep beam through a factor called the strut efficiency factor. Different efficiency factor models have been recommended by various internationally accepted codes. However, none of the codes takes into account the effect of recycled aggregates in concrete. Although some codes yield conservative results, these predictions are not sensitive enough to the recycled aggregate content.  Therefore, an efficiency factor model sensitive to recycled aggregate concrete and easy to operate is much desired. In this work, published results of laboratory tests on deep beam specimens made of concrete consisting of recycled aggregates were considered for the analysis, employing a suitable strut-and-tie model. All these deep beams were originally designed by sectional or empirical method. Based on regression analysis of the outcomes of the STM analysis, an efficiency factor model has been proposed which takes into account the effect of recycled aggregates in concrete. Subsequently, scaled deep beam specimens containing recycled aggregate concrete were cast and tested in the laboratory in order to calibrate the proposed strut efficiency factor model. The yield of proposed efficiency factor model was compared with the predictions of the selected internationally accepted code provisions. It is found that the predictions of proposed efficiency factor model give consistent and comparable results.

Automatic waveform recognition has become an important task in radar systems and spread spectrum communications. Identifying the modulation of received signals helps to recognize different invader transmitters. In this paper, a noise aware model is proposed to recognize the modulation type based on time-frequency characteristics. To this end, Choi-Williams representation is used to obtain spatial 2D pattern of received signal. After that, a deep model is constructed to make signal clear from noise and extract robust and discriminative features from time-frequency pattern, based on auto-encoder and Convolutional Neural Networks (CNN). In order to reduce the effect of noise and adversarial disorders, a new database of different modulation patterns with different AWGN noises and fading Rayleigh channel is created which helps model to avoid the effects of noise on modulation recognition. Our database contains radar modulations such as Barker, LFM, Costas and Frank code which are known as frequently used modulations on wireless communication. Infact, the main novelty of this work is designing this database and proposing noise-aware model. Experimental results demonstrate that the proposed model achieves superior performance for automatic classification recognition with 99.24% of accuracy in noisy medium with minimum SNR of -5dB while the accuracy is 97.90% in SNR of -5dB and f=15 Hz of Doppler frequency. Our model outperforms 5.54% in negative and 0.4% in positive SNRs (even though with less SNR).

In the Internet of Everything era, the Energy Internet of Things (IoT), as a typical application of IoT technology, has been extensively studied. Meanwhile, blockchain technology and energy IoT can be coordinated and complementary. The energy IoT is diversified and has a high transaction demand. it is an issue worthy of research to discuss the impact of the energy IoT environment on the performance of blockchain consensus algorithms and guarantee blockchain stability in energy IoT environment. In the research, an incentive mechanism based on Stackelberg game is proposed for the network scenario involving multiple roadside units and user nodes. The proposed strategy is analyzed through the Matlab simulation platform. The simulation results show that the proposed scheme can effectively protect the interests of blockchain users and miners. It also can improve the security and stability of the blockchain-based energy IoT system. Moreover, the numerical results not only verify the model feasibility. It also shows that when there are many blockchain miners, the model performance is fine. However, when the number of miners reaches a certain value, there will be unobvious growth. Furthermore, it is also confirmed that the wireless energy IoT environment will also create a certain impact on the game model.

Voice activity detectors are presented to extract silence/speech segments of the speech signal to eliminate different background noise signals. A novel voice activity detector is proposed in this paper using spectro-temporal features extracted from the auditory model of the speech signal. After extracting the scale, rate, and frequency features from this feature space, a sparse structured principal component analysis algorithm is used to consider the basic components of these features and reduce the dimension of learning data. Then these feature vectors are employed to learn the models by the sparse non-negative matrix factorization algorithm. The model learning procedure is performed to represent each feature vector with a proper sparse rate based on the selected atoms. Voice activity detection of the input frames is performed by computing the energy of the sparse representation for each input frame over the composite model. If the calculated energy exceeds a specified threshold, it indicates that the input frame has a structure similar to the atoms of the learned models and concludes that the observed frame has voice content. The results of the proposed detector were compared with other baseline methods and classifiers in this processing field. These results in the presence of stationary, non-stationary and periodic noises were investigated and they are shown that the proposed method based on model learning with spectro-temporal features can correctly detect the silence/speech activities.

In this research, the joining of aluminum alloy 5052 to austenitic stainless steel 304 was investigated. For this purpose, friction stir welding process was used in two modes with and without ultrasonic vibrations. In order to achieve the best welding quality in terms of mechanical and metallurgical properties, welding parameters such as rotational speed, linear speed and frequency were investigated. The aim of this research is to obtain a sample with the best mechanical and metallurgical properties and the lowest residual stress. As a research innovation and the aim of measuring the values of residual stress created in the samples after the welding operation, the new method of drilling and Digital Image Correlation was used. Finally, by examining the results, it has been determined that ultrasonic vibrations have improved the mechanical and metallurgical properties about 15% to a large extent. In order to evaluate the accuracy of the results related to the residual stress, all the samples were subjected to the central drilling test by installing a strain gauge, and it was found that the error is less than 10% and obtained results were accurate and appropriate.

This paper presents a behavioral model for noisy Lorenz chaotic synchronization systems. This simple simulation-based model can be used for accurate noise voltage derivation of the chaotic oscillators and the investigation of chaotic synchronization systems. Moreover, the effects of circuitry noise on synchronization of Lorenz systems were analysed by using the proposed model. The performance of the synchronization system was numerically evaluated using ADS and MATLAB-SIMULINK environments. The measurement of Mean Squared Error (MSE) and Error to Noise Ratio (ENR) demonstrates that circuitry noise has a remarkable effect on the performance of chaotic Lorenz synchronization systems. For instance, the results showed that for low Signal to Noise Ratios (SNRs), i.e., , the circuitry noise changed the ENR performance up to 1dB.

This research aims to calibrate and validate the VISSIM simulation model tool by comparing field data with simulation data. The ultimate goal is to evaluate traffic performance by comparing simulation results with direct observations in the field. This study uses modeling to determine a road segment's maximum flow volume. This study was conducted in Makassar, South Sulawesi, Indonesia, on Jalan Veteran Selatan. The method uses two main inputs: urban road primary capacity data from the Indonesian Highway Capacity Manual (IHCM 1997) and roadside activity data from PTV VISSIM. The GEH and MAPE have commonly used metrics for measuring the accuracy of simulation models and calibration measurements using driving behavior parameters. The research results obtained for validation measurements have met the requirements. Namely, the obtained MEPE value (7.38%) is 10% smaller than the obtained GEH value (2.032 and 3.961), which is still more than 5.00. The calibration measurements obtained the suitability of the vehicle location and intervehicle spacing in the simulation model (VISSIM) with the actual field conditions. The results obtained from using VISSIM can be reliable and helpful in designing and optimizing urban transportation systems in the future. It is essential to remember that traffic simulation with VISSIM is only a transportation decision-making and planning tool and must be combined with field observations and accurate data for adequate and efficient transportation solutions.

Integrated energy systems, including renewable energy sources (RES) and battery energy storage (BES), have high potentialities to deal with issues caused by the high penetration of electric vehicles (EVs) in power systems. The full realization of the benefits of such systems depends on implementation of an energy management system (EMS) in order to monitor power sharing between different components of the system. In this paper, an EMS is proposed for a multi-port converter as an integrated PV/BES/EV energy system. It takes into account the EV mileage, BES dis/charge cycles and financial benefits, and schedule for the optimal dis/charge of batteries, and also involves EVs in V2X programs. In this approach, the potential of EVs as a portable energy storage can be employed in providing ancillary services to the power grid. The obvious advantages of the proposed EMS performance have been specified by simulation and comparison with the benchmark method. According to the obtained results, for a specific period of time, a better interaction has been established between the average achievement of the final SOC and the financial profit of the integrated energy system under the proposed EMS. According to the proposed method, for a 10% reduction in the final SOC compared to the benchmark method, the minimum financial benefit is about 0.2607 pounds (received from the grid), equivalent to 0.2082 pounds (paid to the grid) in the benchmark method.

A series of large-scale laboratory model tests in a unit cell was performed to explore the behaviour of loose sandy soil due to improvement. An unreinforced and geogrid reinforced granular blanket, a single end-bearing stone column, and their combination were used for this purpose. Since the rupture of the geosynthetic reinforcement in the reinforced granular blanket has never been experimentally investigated. A novel method of installing the geogrid was used. Thus, geogrid was allowed to completely mobilize and fail under loads. In this investigation, load-settlement characteristics have been generated by continuing loading even after geogrid rupture until the desired settlement. Parametric studies were carried out to observe the effect of important factors, such as the blanket thickness and the layout of geosynthetic sheets, including the number and place of geogrid layers within the granular blanket. Reinforcing the blanket with geogrid while changing the usual form of the load-settlement characteristics has had a significant effect on enhancing load-carrying capacity and reducing settlement. It can be said using a stone column, granular blanket, or combination of both techniques to boost load-carrying capacity was more effective than reducing settlement. However, the effect of single-layer and double-layer geogrid reinforcement on settlement reduction depends on their placement within the granular blanket. In addition, the efficiency of improvement methods has been superior under looser bed conditions. The best layout was to arrange one layer of geogrid near the top of the blanket or two layers in the middle and near the top.

Due to its non-interfering nature, face recognition has been the most suitable technology for designing biometric systems in recent years. This technology is used in various industries, such as health care, education, security, and surveillance. Facial recognition technology works best when a person is looking straight into the camera. On the contrary, the performance of facial recognition degrades when encountered with an angled facial image, because they are generally trained using images of a full face. The purpose of this paper is to estimate the feature vector of a full face image when there are several angular facial images of the same person, one example being angular faces in a video. This method extracts the basic features of a facial image using the non-negative matrix factorization (NMF) method. Then, the feature vectors are fused using a generative adversarial network (GAN) to estimate the feature vector associated with the frontal image. The experimental results on the angular images of the FERET dataset show that the proposed method can significantly improve the accuracy of facial recognition technology methods.

Brain tumor Segmentation is one of the most crucial methods of medical image processing. Non-automatic segmentations are broadly used in clinical diagnosis and medication. However, this kind of segmentation does not have accuracy in medical images, especially in terms of brain tumors, and it provides a low level of reliability. The primary objective of this paper is to develop a methodology for brain tumor segmentation. In this paper, a combination of Convolutional Neural Network and Fuzzy K-means algorithm has been presented to segment the lesion area of brain tumor. It contains three phases, Image preprocessing to reduce computational complexity, Attribute extraction and selection and Segmentation. At first, the database images are pre-processed using adaptive filters and wavelet transform in order to recover the image from the noise state and reduce the computational complexity. Then feature extraction is performed by the proposed deep neural network. Finally, it is processed through the Fuzzy K-Means algorithm to segment the tumor region separately. The innovation of this article is related to the implementation of deep neural network with optimal parameters, identification of related features and removal of unrelated and repetitive features with the aim of observing a subset of features that describe the problem well and with minimal reduction in efficiency. This results in reduced feature sets, storage of data collection resources during operation, and overall data reduction to limit storage requirements. This proposed segmentation approach has been verified on BRATS dataset and produces the accuracy of 98.64%, sensitivity of 100% specificity of 99%.

Radiology report generation is a critical task for radiologists, and automating the process can significantly simplify their workload. However, creating accurate and reliable radiology reports requires radiologists to have sufficient experience and time to review medical images. Unfortunately, many radiology reports end with ambiguous conclusions, resulting in additional testing and diagnostic procedures for patients. To address this, we proposed an encoder-decoder-based deep learning framework that utilizes chest X-ray images to produce diagnostic radiology reports. In our study, we have introduced a novel text modelling and visual feature extraction strategy as part of our proposed encoder-decoder-based deep learning framework. Our approach aims to extract essential visual and textual information from chest X-ray images to generate more accurate and reliable radiology reports. Additionally, we have developed a dynamic web portal that accepts chest X-rays as input and generates a radiology report as output. We conducted an extensive analysis of our model and compared its performance with other state-of-the-art deep learning approaches. Our findings indicate significant improvement achieved by our proposed model compared to existing models, as evidenced by the higher BLEU scores (BLEU1 = 0.588, BLEU2 = 0.4325, BLEU3 = 0.4017, BLEU4 = 0.3860) attained on the Indiana University Dataset. These results underscore the potential of our deep learning framework to enhance the accuracy and reliability of radiology reports, leading to more efficient and effective medical treatment.

In this paper, integrated control of highways and intersections is investigated. A modular Petri-Net-based framework is implemented to model the traffic flow of highway and arterial traffic network systems. In this framework, arterial intersection traffic lights are modeled by Timed Petri Nets (TPN). The timing of traffic lights and variable speed limits on the highway is managed to be optimized using an intelligent algorithm. This algorithm provides a trade-off between the length of the queue of vehicles on the highway and the entrance ramp and the length of the queue at the intersection after each time cycle. The performance of the optimized traffic controller and the fixed control were compared. The simulation results verify that the use of optimization methods to manage the timing of traffic lights in intersections and speed limitation in highways can considerably improve traffic flow in special conditions such as rainy weather and accidents. Additionally, this method can considerably enhance traffic flow in normal hours, while in rush hours and midnight, such improvement is negligible.