Emotion recognition and analysis of netizens based on micro-blog during covid-19 epidemic

The research is about emotion recognition and analysis based on Micro-blog short text. Emotion recognition is an important field of text classification in Natural Language Processing. The data of this research comes from Micro-blog 100K record related to COVID-19 theme collected by Data fountain platform, the data are manually labeled, and the emotional tendencies of the text are negative, positive and neutral. The empirical part adopts dictionary emotion recognition method and machine learning emotion recognition respectively. The algorithms used include support vector machine and naive Bayes based on TFIDF, support vector machine and LSTM based on wod2vec. The five results are compared. Combined with statistical analysis methods, the emotions of netizens in the early stage of the epidemic are analyzed for public opinion. This research uses machine learning algorithm combined with statistical analysis to analyze current events in real time. It will be of great significance for the introduction and implementation of national policies

Determining Damaging Fatigue Cycles under Influence of Random Loadings using the Root-Mean-Square Level / M. Mahmud ...[et al.]

The aim of this paper is to investigate the damaging fatigue cycles criterion using the root-mean-square level under the influence of random loads for coil spring. Fatigue life cycle analysis especially in signal processing involves high computational effort because it deals with large quantity of data from vibratory loads obtained from the coil spring. The captured data of frequent low amplitude cycles generally consist of noise or vibrations which are meaningless and not significant for analysis. Therefore, a criterion using the root-mean-square level is proposed in assessing fatigue life of the captured strain signal from the coil spring. Four strain signals were analysed statistically using global statistics and distribution fitting. Fatigue damage was determined using the Morrow model and control charts were used in the classification of predefined damaging cycles. For evaluating the contribution of these cycles to fatigue damage, cycle elimination process was performed. The results showed a significant reduction of 48%−62% in damage values with damage probability ranging from 0.9362 to 0.9999. Hence, the criterion is useful and has potential to be extended in determining damaging cycles in fatigue analysis in indicating the damaging effects for coil spring

Classification of Fatigue Damaging Segments Using Artificial Neural Network / M. F. M. Yunoh ...[et al.]

This paper focuses on the classification of the fatigue damaging segments datasets associated with the measurement of Variable Amplitude Loadings of strain signals from the coil springs of an automobile during road tests. The wavelet transform was used to extract high damaging segments of the fatigue strain signals. The parameters of the kurtosis, wavelet-based coefficients, and fatigue damage were then calculated for every segment. All the parameters were used as input for the classification analysis using artificial neural networks. Using the back-propagation trained artificial neural network, the corresponding fatigue damages were classified. It was observed that the classification method was able to give 100% accuracy on the classifications based on the damaging segments that were extracted from the training and the validation datasets. From this approach, it classified the level of fatigue damage for coils spring

Use of a Combination of MRSS-ANP for Making an Innovative Landfill Siting Decision Model

Landfill siting is a complex, multicriteria decision-making problem that needs an extensive evaluation of environmental, social, land use, and operational criteria. Integration of a median ranked sample set (MRSS) and an analytic network process (ANP) has been implemented to rank the associated criteria and select a suitable landfill site. It minimizes the uncertainty and the subjectivity of human judgments. Four groups of experts with different backgrounds participated in this study, and each group contained four experts. The respondent preferences were ranked in a 4-by-4 matrix to obtain the judgment sets for the MRSS. These sets were subsequently analyzed using ANP to obtain the priorities in the landfill siting criteria. The results show that land topology and distance from surface water are the most influential factors, with priorities of 0.18 and 0.17, respectively. The proposed integrated model may become a promising tool for the environmental planners and decision makers

EVALUATING THE RELIABILITY OF PRE-TEST DIFFERENTIAL EQUATIONS QUESTIONS USING RASCH MEASUREMENT MODEL

A good exam questions should be able to gauge student’s understanding and achievement related to Course Outcome (CO), Bloom’s Taxonomy level and Programme Outcome (PO). To achieve this, a set of pre-test questions were prepared to evaluate the pre achievement level among the students related to CO, PO and the Bloom’s Taxonomy level. In this study, a pre-test for Differential Equations (KKKQ2123) was given to 100 second year students from the department of Electrical, Electronic and Systems Engineering. The level of Bloom’s Taxonomy measured consists of level 1 (knowledge) to level 6 (creation). Rasch Measurement Model was applied to analyse the reliability of the pre-test questions. The analysis revealed that all the pre-test questions were reliable and no questions were found unsuitable. Prior assessment (pre-test) is important in the preparation of final exam questions as it would indicate the level of student’s understanding in a particular topic that relates to the CO and PO of the programme

Proceedings of Computational & Experimental Mechanics (CEM 2007)

The multiobjective optimisation for the case of increasing performance and controlling exhaust emissions in a compressed natural gas direct injection engine is presented in the present paper. The three combustion-controlled parameters affected to the engine performance and their emissions are start of injection (SOI), end of injection (EOI) and spark advance (SA) timings. The engine performance considered as the objective is power as expressed by its cylinder pressure, while CO and NO emissions level are taken into account as the most hazardous pollutants. The multidimensional engine modelling and combustion simulation was carried out by mean of computational fluid dynamics (CFD) code. In order to develop the response surface of design parameters, the face centred cubic (FCC) method as a design of experiment technique was used. On the other hand, the multiobjective optimization tool in the term of the Gaussian processes (GP) and multiobjective genetic algorithm (MOGA) was coupled with the CFD code to increase engine power and control emissions level to the acceptable standard level compared to gasoline-fuelled engine. The GP was employed to predict the values of engine parameters based on the response surface established by FCC method. The engine speed and operating conditions at 3000 rpm as a medium speed of light-duty vehicle was considered to be analysed. Based on the comparisons performed, ten Pareto designs can be chosen to represent the improved engine parameters for the result of improvement in performance and its emissions. Furthermore, the multicriteria decision making (MCDM) analysis was carried out to select one optimum Pareto design at such engine speed. The implementation of artificial intelligence method for the optimisation of engine parameters in an internal combustion engine can be proposed to replace the experimental test rig virtually. In addition, the CFD time required for in-cylinder combustion simulation can be reduced dramatically

Proceedings of International Advanced Technology Congress (ATCi 2005)

The design and development of an internal combustion (IC) engine requires the application of advanced analysis and development tools to carry out the investigation of an in-depth investigation and analysis into the complex engine simulation by using the computational fluid dynamics (CFD) code. In this paper, the computational method of moving meshes for 1.6-litre 4-cylinder automotive four-stroke engine forthe transient condition has been developed and performed which includes the valve, piston and cylinder movement for multi-cylinder of the engine model. The purpose of developing these moving meshes were to verify the computational algorithm of moving meshes and determine the positions of intake valve, exhaust valve, engine cylinder and the piston in the combustion chamber with respect to crank angle. The executed simulation covers the full engine cycle consisting of the intake, compression, power and exhaust stroke with the certain firing order according to the engine specification. The mesh movements for this simulation were established using the CFD code via declaring the events and activating the moving grid command. Finally, the meshes of the engine model could be obtained successfully at every crank angle during the simulation of the full cycle of multi-cylinder engine model

Proceedings of International Advanced Technology Congress (ATCi 2005)

The development of an internal combustion engine (ICE) requires the application of advanced analysis and tools, where numerical prediction by using the computational fluid dynamics (CFD) code can be utilised in order to perform in-depth investigation into complex engine behaviour. The important source of air pollution from the combustion process of ICE are carbon monoxide (CO) and nitrogen oxide (NOx), which needed to be analysed to reduce the engine emission. Hence, this paper presents numerical simulation of concentrations of exhaust emission produced during the combustion process for in the compressed natural gas-direct injection (CNG-DI) engine. The types of emission in this study were limited to only CO and nitric oxide (NO) as the most dangerous emission pollutant produced by such engine. The CO and NO concentrations are computed using the dissociation of carbon dioxide and three extended Zeldovich mechanisms. The validation data was performed by comparing the experimental result at the intake manifold using a gas analyser and numerical result of CFD calculation along the combustion process are presented