The paper deals with the isogeometric analysis (IGA) of active composite laminates with piezoelectric layers. IGA is a special formulation of the finite element method (FEM) that aims at seamless ...integration of geometric and finite element modelling. NURBS basis functions are employed to develop isogeometric shell formulation based on the Reissner-Mindlin kinematics. Piezolayers characterized by electro-mechanical coupled field effects enable active behavior of the considered structures. The electric field acts across the thickness of the piezolayers and is coupled to the in-plane strains. In addition to a number of advantages that NURBS modelling provides, defining the surface normal vector at the points of the control polygon, which are generally not located on the surface, creates certain difficulties. A method of determining the surface normal vectors at the points of the control polygon based on the Greville's points is discussed. In order to demonstrate the applicability of the developed formulation, a benchmark case is computed and the results are compared with those obtained by means of classical FEM formulation, which are available in the literature.
Diesel engines are economical thanks to their combustion process characteristics, which is why they have a high noise emission level as well as exhaust emissions of nitrogen oxide and particulate ...matters. By continuously changing the value of compression ratio, it is possible to control the power and emissions. Implementation of variable compression ratio has many benefits, such as being able to work with different types of fuel. In this way, it is possible to optimize the combustion process for operation with minimum fuel consumption and emission generation, so that diesel engines can be applied to the framework of future hybrid vehicle concepts, and so forth. As far as the crucial objective of the manuscript is concerned, experimental diesel engine investigation was performed on a roller test-bench by using zero-dimensional computer model (specifically AVL IndiCom Indicate Software). Engine indication was executed with the factory compression ratio value and with three lower values. During our examination, the change in the compression ratio value was achieved by changing the volume of a combustion chamber at a piston-bowl. The results of laboratory research on the experimental engine are presented in the paper when discussing a series of specific parameters (characteristics), such as compression ratio, fuel injection timing, engine speed, as well as load influence on combustion process and exhaust emissions.
In the modern era, Internet of Things (IoT) has been a popular research topic and it focuses on interconnecting numerous sensor-based devices primarily for tracking applications and collecting data. ...Wireless Sensor Networks (WSN) becomes a significant element in IoT platforms since its inception and turns out to be the most ideal platform for deploying various smart city application zones namely disaster management, home automation, intelligent transportation, smart buildings, and other IoT-enabled applications. Clustering techniques were commonly used energy-efficient methods with the main purpose that is to balance the energy between Sensor Nodes (SN). Routing and clustering are Non-Polynomial (NP) hard issues where bio-inspired approaches were used for a known time to solve these issues. This study introduces a Hybrid Sine-Cosine Black Widow Spider Optimization based Route Selection Protocol (HSBWSO-RSP) for Mulithop Communication in IoT assisted WSN. The presented HSBWSO-RSP technique aims to properly determine the routes to destination for multihop communication. Moreover, the HSBWSO-RSP approach enables the integration of variance perturbation mechanism into the traditional BWSO algorithm. Furthermore, the selection of routes takes place by a fitness function comprising Residual Energy (RE) and distance (DIST). The experimental result analysis of the HSBWSO-RSP technique is tested using a series of experimentations and the results are studied under different measures. The proposed methodology achieves 100% packet delivery ratio, no packet loss and 2.33 secs end to end delay. The comparison study revealed the betterment of the HSBWSO-RSP technique over existing routing techniques.
On the internet, various devices that are connected to the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) share the resources that they have in accordance with their respective needs. ...The information gathered from these Internet of Things devices was preserved in the cloud. The problem of latency is made significantly worse by the proliferation of Internet of Things devices and the accessing of real-time data. In order to solve this issue, the fog layer, which was previously an adjunct layer between the cloud layer and the user, is now being utilised. As the data could be retrieved from the fog layer even if it was close to the edge of the network, it made the experience more convenient for the user. The lack of security in the fog layer is going to be an issue. The simple access to sources provided by the fog layer architecture makes it vulnerable to a great number of assaults. Consequently, the purpose of this work is to build a seagull optimization-based feature selection approach with optimum extreme learning machine (SGOFS-OELM) for the purpose of intrusion detection in a fog-enabled WSN. The identification of intrusions in the fog-enabled WSN is the primary focus of the SGOFS-OELM approach that has been presented here. The given SGOFS-OELM strategy is designed to accomplish this goal by designing the SGOFS approach to choose the best possible subset of attributes. In this work, the ELM classification model is applied for the purpose of intrusion detection. In conclusion, the political optimizer (PO) is utilised in order to accomplish automatic parameter adjustment of the ELM technique, which ultimately leads to enhanced classification performance. In order to demonstrate the usefulness of the SGOFS-OELM approach, a number of simulations were carried out. As compared to the other benchmark models that were employed for this research, the suggested SGOFS-OELM models give the best accuracy, which is 99.97 percent. The simulation research demonstrates that the SGOFS-OELM approach has the potential to deliver a good performance in the intrusion detection process.
In order to solve the classification model's shortcomings, this study suggests a new trash classification model that is generated by altering the structure of the ResNet-50 network. The improvement ...is divided into two sections. The first section is to change the residual block. To filter the input features, the attention module is inserted into the residual block. Simultaneously, the downsampling process in the residual block is changed to decrease information loss. The second section is multi-scale feature fusion. To optimize feature usage, horizontal and vertical multi-scale feature fusion is integrated to the primary network structure. Because of the filtering and reuse of image features, the enhanced model can achieve higher classification performance than existing models for small data sets with few samples. The experimental results show that the modified model outperforms the original ResNet-50 model on the TrashNet dataset by 7.62% and is more robust. In the meanwhile, our model is more accurate than other advanced methods.
Strict requirements that are put on mechanical constructions from the aspect of increase of exploitation periods and reduction of their weights, therefore of their prices as well, implicate ...developments and applications of new composite materials with matrices of lightweight metals. Composite materials with metal matrices are used for engine cylinders, pistons, disc and drum brakes, Cardan shafts and for other elements in automotive and aviation industry. The most important type of metallic materials is composite materials with matrices of aluminium alloys due to a set of their beneficial properties. Improvement of mechanical, especially tribological properties of hybrid composites were provided by the use of certain reinforce materials such as SiC, Al sub(2)O sub(3) and graphite in defined weight or volumetric share. New developed hybrid composites with aluminium matrices have significantly higher resistance to wear, higher specific stiffness and higher resistance to fatigue. By the increase of quantities of produced elements made of hybrid composites, decrease of their prices is induced that even further enlarge their applications. The applications of aluminium hybrid composites are considered from the aspect and with the focus on automotive industry.Original Abstract: Poostravanje konstrukcijskih zahtjeva s aspekta povecanja radnog vijeka i smanjenja mase, a samim tim i cijene konstrukcije, iniciralo je razvoj i primjenu novih materijala s osnovom od lakih metala. Kompoziti s metalnom matricom nalaze sve vecu primjenu pri izradi kosuljica cilindara motora, klipova, kocionih diskova i dobosa, kardanskih vratila kao i drugih dijelova u automobilskoj i avio industriji. Posebno mjesto, od svih metalnih materijala, zauzimaju kompoziti na osnovi legure aluminija zbog niza dobrih svojstava. Poboljsanje mehanickih, a posebice triboloskih karakteristika hibridnih kompozita moguce je uporabom odredenih ojacivaca, najcesce SiC, Al sub(2)O sub(3) i grafita, u odgovarajucem masenom ili volumenskom udjelu. Novodobiveni hibridni kompoziti s aluminijskom osnovom imaju znatno povecanu otpornost na habanje, povecanu specificnu krutost i povecanu otpornost na zamor. Povecanjem kolicine proizvedenih dijelova od aluminijskih kompozitnih materijala dolazi do smanjenja cijene ovih dijelova, sto dodatno povecava podrucje njihove primjene. U radu su prikazani primjeri primjene aluminijskih hibridnih kompozita s osvrtom i tezistem na automobilskoj industriji.
Under the data-driven environment, market competition is increasingly fierce. Enterprises begin to pay attention to precise marketing to make costs down, improve marketing efficiency and ...competitiveness. E-mail marketing is widely used in enterprises due to its advantages of low cost and wide audience. This paper uses machine-learning techniques such as decision tree, cluster analysis and Naive Bayes algorithm to analyze customer characteristics and attributes with historical purchase records, and further analyzes the key factors that affect potential customers' purchase behavior by selecting models with high promotion degree through promotion graph, to realize accurate marketing. The results show that the prediction effect of decision tree is better than clustering analysis and Naive Bayesian algorithm, and has a higher promotion degree. The customers who are 45-55 years old and commute 1-2 kilometers away are more likely to make purchases if they do not have a car or have a car at home.
Today, cancer has become a common disease that can afflict the life of one of every three people. Breast cancer is also one of the cancer types for which early diagnosis and detection is especially ...important. The earlier breast cancer is detected, the higher the chances of the patient being treated. Therefore, many early detection or prediction methods are being investigated and used in the fight against breast cancer. In this paper, the aim was to predict and detect breast cancer early with non-invasive and painless methods that use data mining algorithms. All the data mining classification algorithms in Weka were run and compared against a data set obtained from the measurements of an antenna consisting of frequency bandwidth, dielectric constant of the antenna's substrate, electric field and tumor information for breast cancer detection and prediction. Results indicate that Bagging, IBk, Random Committee, Random Forest, and SimpleCART algorithms were the most successful algorithms, with over 90% accuracy in detection. This comparative study of several classification algorithms for breast cancer diagnosis using a data set from the measurements of an antenna with a 10-fold cross-validation method provided a perspective into the data mining methods' ability of relative prediction. From data obtained in this study it can be said that if a patient has a breast cancer tumor, detection of the tumor is possible.
The fourth industrial revolution enhanced the development of information technology in all fields and opened up possibilities. A lot of attention is focused on the future possibilities opened up by ...the metaverse, the core of information technology. Metaverse will have a big impact on reality and the near future. Metaverse is a virtual world that fuses physical and digital reality. Various commerce such as healthcare, instruction, business, and land are foundation to utilize metaverse knowledge in their regular work. There is a series of processes in the stage where newly developed technology is introduced to general users. In order for a new technology to become a user-friendly technology, it is necessary to verify the technology. It can be said that it is hard to derive the operator's usage intention in a state where user trust for new technology is not verified. In the metaverse environment, it is necessary to first verify the trust for new technologies. This study is expected to understand usage intention through the process of checking trust in metaverse, and to become basic data for the popularization of metaverse knowledge. The meaning of this research is to inspect the influence relationship of trust in metaverse on usage intention through Technology Readiness (TR) and Technology Acceptance Model (TAM). Statistical package (SPSS23.0) was used for basic numerical examination of the questionnaire. Hypothesis test was performed using the structural equation package Smart PLS 3.0. Discriminant validity and concentration validity of the questionnaire were verified. As parameters that trust in metaverse effects, TR and TAM were set. As factors constituting TR, it was separated into optimism, innovativeness, discomfort, and insecurity. The TAM is separated into perceived usefulness and perceived ease of use. The outcomes of the study are as follows. First, trust in metaverse had a significant effect on TR. Second, TR was partially adopted in the TAM. Innovativeness and perceived usefulness had no significant effect. Third, TAM significantly influences usage intention. Fourth, perceived ease of use did not significantly influence perceived usefulness.
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