With the increase in population and the rapid development of industrialization, there has been an increase in the use of fossil fuels. As a result of this use, some harmful gases and materials such as sulfur dioxide, carbon dioxide and particles are emitted to the environment. If these harmful pollutants are not cleaned, the air quality of the relevant place will be adversely affected. Especially, environmental pollution has occurred with harmful particle release from the flues in the facilities. In addition, heat release in the flue is lost for the system. In this study, it is aimed to decrease pollution and to contribute to energy efficiency by using electrofilter, which is an electronic method. Experimental studies are carried out on the prototype created in the laboratory. The performance of electrofilter is studied for different properties of electrodes such as width, length, and the number. According to the results, it is seen that the amount of emission can be reduced by increasing the electrode surface area. Electricity can also be generated by harvesting waste heat inside the flue with appropriate electronic equipment and it can be used to energize the system when necessary. Besides, prototype has a user interface and wireless communication features. Thus, an environmentally friendly and remotely observable system is acquired.
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The simulation includes enormous technological tools and methods for the successful implementation of digital manufacturing as it allows product, process, system design, and configuration to be tested and verified. With the growth in the industrial sector, information systems and software products play an important role in the investments of technology in automation. The industrial sector has enabled the use of simulations to directly optimize the design and production of systems with computational growth. Modeling takes advantage of the integration of computing and controlling, enabling intelligent production and sustainability to greatly increase the efficiency of industrial systems. Recent technologies have been focusing on the development of solutions for increasing customer expectations, competition conditions, product and service providers. The information used for production is integrated with the simulation requirements in association with other software platforms in a flexible manner. In industry 4.0, various modeling and simulation techniques for automation of the industrial process in the integration of service-oriented architecture into contemporary management systems are presented and the modeling techniques available in the literature are discussed. Important milestones of simulation technologies in product, process and system design of digitized manufacturing systems have been investigated and the latest industrial and research approaches in basic manufacturing areas have been examined. It explores important milestones in the evolution of simulation technologies and the latest industrial and research applications and findings. Based on this review, future trends and challenges to be met in the field are summarized in order to reduce costs in current practices, increase quality and shorten the time to market of manufactured goods.
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In this paper, vehicle mass estimation problem was researched by the Kalman Filtering process to investigate the effectiveness of Extended Kalman Filter technique on mass estimation, which has widely been studied on the related literature with all the successful results. For the purpose, a longitudinal vehicle dynamics model was developed, and the equations were transformed into the well-known state space form. Nonlinear problem in its nature was discretised and linearized by using Euler method. In addition, road slope was calculated by a road slope inclinometer logic. IPG CarMaker® was utilized to overcome the nonlinearities, design the test environment and the conditions, as it was observed that the simulation results were not met the expectations with the logged vehicle physical test data. Several test scenarios were run to simulate level and wavy road with different vehicle speeds. The results showed that the Extended Kalman Filter could provide a reasonable convergence when the initial values were correctly selected, no nonlinear issue occurred and certain tuning parameters were defined with stable boundary conditions for mass estimation.
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According to the reports of Public Health Institution, approximately 250,000 rabies-risk animal bites occur per year in Turkey. Most of these bites are caused by dogs and most of the victims are the children who play in playgrounds. With the development of deep learning-based computer vision technology, autonomous detection of dangerous objects (handguns, knives, dogs, etc.) in these children’s playgrounds has become a crucial security application. In this paper, a real-time dog detection model based on YOLOv3 deep learning algorithm is proposed as a new smart city security application and this model is applied to the selected children’s playground. Firstly, in view of the problem of insufficient stray dog image data in the original datasets, new images of stray dogs have been taken from an animal shelter and they have been added to the dataset. These new images have effectively enriched the diversity of training data and improved the training performance of the proposed model. The proposed model has been optimized by utilizing various hyperparameters and the results have been compared with each other. The model with the best evaluation scores is proposed and applied to detect dogs automatically by the fast emergency station located in the selected children’s playground. The real-time application has achieved 82.59% of AP with adjusted hyperparameters.
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Biomimetic is the name of the approach that seeks sustainable solutions to the problems, taking the perfect functioning of nature for millions of years. The interest shown in biomimetic surfaces, inspired by multi-scale structures found in many plants and animals, is increasing day by day. Especially the unique wettability properties of the lotus leaf and rose petal. In this study, inspired by the structures of lotus leaf and rose petal, using the soft casting method with dental bio-polymer materials, structures with pillar dimensions of micron (µm) and millimeter (mm) were produced. These structures are replicated from two commercial products with different pillar lengths and different pillar shapes, mushroom and conical needle tips. Surface topographies of the replicated final products were analyzed by optical and stereo microscopes. Contact angles were tested to examine the wettability properties of the surfaces. According to the microscope results obtained, the demolding process, which is the riskiest step of the replication process, was successfully passed thanks to soft casting. Contact angle analysis showed that different pillar lengths and different pillar shapes changed the wettability properties of the replicated final product. The replicated mushroom-shaped micron-scale pillar structures exhibited a rose-petal effect and hydrophobic properties (approximately 1000) with only a single-scale configuration, while conical needle-shaped pillars of millimeter (mm) scale did not show any specific wetabilitty property.
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Pervaporative desalination is an efficacious method to obtain fresh water from saline water sources. High salt rejection and capacity to cope with high-salt concentration water solutions are important advantages of pervaporation process. To improve pervaporative desalination performance, researches concentrate on novel membrane types. Water stable metal organic frameworks loaded membranes attract substantial attention among novelty membrane applications. This study focuses on pervaporative desalination with MOF loaded polymeric membrane. First of all, the features of MOFs, synthesis methods, usage areas are explained. Then, MOFs loaded mixed matrix membranes, production methods, used MOF types in membranes and applications in pervaporative desalination are examined. At the end of the study, future expectation on the desalination applications of MOF loaded membranes are presented as concluding remarks. Metal organic framework loaded polymeric membranes are seen as promising candidates to obtain drinking water with high separation yield in pervaporative desalination process.
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Data collection is one of the key building blocks of smart city applications. Sheer number of sensors deployed across the city generate huge amount of data continuously. Due to their limited transmission range, sensors form a sensor network with a base station. The base station acts as a gateway between the network and the remote user and the generated data is collected by the base station. However, due to sensor locations and the transmission range the network may consist of several partitions. A typical solution is employing one or more mobile element(s) to collect data from partitions periodically. Mobile data collection enables intermittent connectivity between sensors and the base station. The major drawback of mobile data collection is increased data latency depending on the velocity of the mobiles. Another challenge is specifying importance for individual sensors in a smart city application. This study evaluates the impact of precedence-based routing of mobiles on data latency in a realistic manner through employing spatial data obtained from a geographic information system. Precedence levels for sensors are determined based on the amenity type of the building they monitor. Mobility of the mobiles is restricted with the drivable road network. The impact of the precedence-based routing according to total path length, maximum data collection delay, and the maximum data latency is evaluated. Obtained results indicate an increase in total path length up to 14% when precedence-based routing is applied. The results also suggest that precedence-based routing increases maximum data collection delay unless the amenity type has fewer points of interest to monitor.
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$TiB_2$ (titanium diboride) is a transition metal boride with remarkable properties and, its thinfilm coatings can be deposited on various substrates to develop the wear resistance properties
of substrates. Radiation interaction properties of $TiB_2$ coated glass and HSS are very
significant as well for shielding applications and it has not been investigated so far. In this
work, linear attenuation coefficient (µ), half-value layer (HVL), tenth-value layer (TVL) and
mean free path (MFP) of TiB2 coated glass and HSS (AISI-M2) were measured using a $^{133}Ba$
radioactive point source at energies 80.8, 276.4, 302.8, 356 and 383.8 keV. A comparison
has been made with some radiation shielding concretes with respect to MFP. Energy
absorption and exposure buildup factors (EABF and EBF) of composites were also calculated
in the experimental energy region 50 – 500 keV. $TiB_2$ coated glass and HSS were found to
be better radiation shielding materials than the standard shielding concretes concluding that
they can be further developed for radiation shielding applications.
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Data mining is an interdisciplinary field that uses methods such as machine learning,
artificial intelligence, statistics, and deep learning. Classification is an important data
mining technique as it is widely used by researchers. Generally, statistical methods or
machine learning algorithms such as Decision Trees, Fuzzy Logic, Genetic
Programming, Random Forest, Artificial Neural Networks and Logistic Regression have
been used in software defect prediction in the literature. Performance measures such as
Accuracy, Precision, Mean Absolute Error (MAE) and Root Mean Squared Error
(RMSE) are used to examine the performance of these classifiers. In this paper, 4 data
sets entitled JM1, KC1, CM1, PC1 in the PROMISE repository, which are created within
the scope of the publicly available NASA institution's Metric Data Program, are
examined as in the other software defect prediction studies in the literature. These
datasets include Halstead, McCabe method-level, and some other class-level metrics.
Data sets are used with Wakiato Environment for Knowledge Analysis (WEKA) data
mining software tool. By this tool, some classification algorithms such as Naive Bayes,
SMO, K *, AdaBoost1, J48 and Random Forest were applied on NASA error datasets in
PROMISE repository and their accuracy rates were compared. The best value among the
accuracy rates was obtained in the Bagging algorithm in the PC1 data set with the values
of %94.13.
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In this paper, a new CNN architecture is introduced for classification of six different hand gestures using surface electromyography (EMG) data collected from the forearm. At first, two different deep neural networks produced based on Slow Fusion and Inception models separately. Then, the average of accuracy values and standard deviations were calculated for each type of network. The average accuracy was 80.88% and standard deviation was 0.030 for the Slow Fusion based network. For the Inception based network, average accuracy was 82.64% and standard deviation was 0.028. In addition to these two networks, a new CNN architecture is introduced using Slow fusion and Inception models in combination. The architecture has two parallel Inception modules in parallel. Each parallel module is fed by the half of the 3D feature map. The proposed model slowly fuses the information of the parallel modules throughout the network as in Slow-Fusion architecture. The average accuracy achieved with this model was 83.97% and the standard deviation was 0.027. Despite the small data set, the accuracy had increased with the proposed hybrid model. The smaller standard deviation indicates that it is less affected by variations in the training dataset. Our experimental results show that the proposed method gives the best results among the Slow Fusion based and Inception based models.
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