Search results for: adaptive behaviours

Authors: Amir Ahmad Dehghani, Morteza Nabizadeh

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This paper presents the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to flood hydrograph modeling of Shahid Rajaee reservoir dam located in Iran. This was carried out using 11 flood hydrographs recorded in Tajan river gauging station. From this dataset, 9 flood hydrographs were chosen to train the model and 2 flood hydrographs to test the model. The different architectures of neuro-fuzzy model according to the membership function and learning algorithm were designed and trained with different epochs. The results were evaluated in comparison with the observed hydrographs and the best structure of model was chosen according the least RMSE in each performance. To evaluate the efficiency of neuro-fuzzy model, various statistical indices such as Nash-Sutcliff and flood peak discharge error criteria were calculated. In this simulation, the coordinates of a flood hydrograph including peak discharge were estimated using the discharge values occurred in the earlier time steps as input values to the neuro-fuzzy model. These results indicate the satisfactory efficiency of neuro-fuzzy model for flood simulating. This performance of the model demonstrates the suitability of the implemented approach to flood management projects.

Keywords: adaptive neuro-fuzzy inference system, flood hydrograph, hybrid learning algorithm, Shahid Rajaee reservoir dam

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Authors: Daniel M. Muntean, Viorel Ungureanu

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More than half of the urban population in Romania lives today in residential buildings made out of large prefabricated reinforced concrete panels. Since their initial design was made in the 1960’s, these housing units are now being technically and morally outdated, consuming large amounts of energy for heating, cooling, ventilation and lighting, while failing to meet the needs of the contemporary life-style. Due to their widespread use, the design of a system that improves their energy efficiency would have a real impact, not only on the energy consumption of the residential sector, but also on the quality of life that it offers. Furthermore, with the transition of today’s existing power grid to a “smart grid”, buildings could become an active element for future electricity networks by contributing in micro-generation and energy storage. One of the most addressed issues today is to find locally adapted strategies that can be applied considering the 20-20-20 EU policy criteria and to offer sustainable and innovative solutions for the cost-optimal energy performance of buildings adapted on the existing local market. This paper presents a possible adaptive design scenario towards sustainable retrofitting of these housing units. The apartments are transformed in order to meet the current living requirements and additional extensions are placed on top of the building, replacing the unused roof space, acting not only as housing units, but as active solar energy collection systems. An adaptive building envelope is ensured in order to achieve overall air-tightness and an elevator system is introduced to facilitate access to the upper levels.

Keywords: adaptive building, energy efficiency, retrofitting, residential buildings, smart grid

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Authors: Khizir Mohd Ismail, Yu Jun Lim, Tshun Howe Yong

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The study of flow behavior in an enclosed volume using Computational Fluid Dynamics (CFD) has been around for decades. However, due to the knowledge limitation of adaptive grid methods, the flow in an enclosed volume near the moving wall using CFD is less explored. A CFD simulation of flow in an enclosed volume near a moving wall was demonstrated and studied by introducing a moving mesh method and was modeled with Unsteady Reynolds-Averaged Navier-Stokes (URANS) approach. A static enclosed volume with controlled opening size in the bottom was positioned against a moving, translational wall with sliding mesh features. Controlled variables such as smoothed, crevices and corrugated wall characteristics, the distance between the enclosed volume to the wall and the moving wall speed against the enclosed chamber were varied to understand how the flow behaves and reacts in between these two geometries. These model simulations were validated against experimental results and provided result confidence when the simulation had shown good agreement with the experimental data. This study had provided better insight into the flow behaving in an enclosed volume when various wall types in motion were introduced within the various distance between each other and create a potential opportunity of application which involves adaptive grid methods in CFD.

Keywords: moving wall, adaptive grid methods, CFD, moving mesh method

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Authors: Seung Ho Park, Young Su Moon, Nam Ik Cho

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We propose a convolution neural network (CNN) for quality adaptive compression artifact reduction named QCARNet. The proposed method is different from the existing discriminative models that learn a specific model at a certain quality level. The method is composed of a quality estimation CNN (QECNN) and a compression artifact reduction CNN (CARCNN), which are two functionally separate CNNs. By connecting the QECNN and CARCNN, each CARCNN layer is able to adaptively reduce compression artifacts and preserve details depending on the estimated quality level map generated by the QECNN. We experimentally demonstrate that the proposed method achieves better performance compared to other state-of-the-art blind compression artifact reduction methods.

Keywords: compression artifact reduction, deblocking, image denoising, image restoration

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Authors: Bin Zhou, Tongli Lu, Jianwu Zhang, Hongtao Hao

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Dual clutch transmissions (DCT) offer a high comfort performance in terms of the gearshift. Hydraulic multi-disk clutches are the key components of DCT, its engagement determines the shifting comfort. The prefill of the clutches requests an initial engagement which the clutches just contact against each other but not transmit substantial torque from the engine, this initial clutch engagement point is called the touch point. Open-loop control is typically implemented for the clutch prefill, a lot of uncertainties, such as oil temperature and clutch wear, significantly affects the prefill, probably resulting in an inappropriate touch point. Underfill causes the engine flaring in gearshift while overfill arises clutch tying up, both deteriorating the shifting comfort of DCT. Therefore, it is important to enable an adaptive capacity for the clutch prefills regarding the uncertainties. In this paper, a dynamic model of the hydraulic actuator system is presented, including the variable force solenoid and clutch piston, and validated by a test. Subsequently, the open-loop clutch prefill is simulated based on the proposed model. Two control parameters of the prefill, fast fill time and stable fill pressure is analyzed with regard to the impact on the prefill. The former has great effects on the pressure transients, the latter directly influences the touch point. Finally, an adaptive method is proposed for the clutch prefill during gear shifting, in which clutch fill control parameters are adjusted adaptively and continually. The adaptive strategy is changing the stable fill pressure according to the current clutch slip during a gearshift, improving the next prefill process. The stable fill pressure is increased by means of the clutch slip while underfill and decreased with a constant value for overfill. The entire strategy is designed in the Simulink/Stateflow, and implemented in the transmission control unit with optimization. Road vehicle test results have shown the strategy realized its adaptive capability and proven it improves the shifting comfort.

Keywords: clutch prefill, clutch slip, dual clutch transmission, touch point, variable force solenoid

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Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

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Authors: Agostinho Antunes

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The completion of the human genome sequencing in 2003 opened a new perspective into the importance of whole genome sequencing projects, and currently multiple species are having their genomes completed sequenced, from simple organisms, such as bacteria, to more complex taxa, such as mammals. This voluminous sequencing data generated across multiple organisms provides also the framework to better understand the genetic makeup of such species and related ones, allowing to explore the genetic changes underlining the evolution of diverse phenotypic traits. Here, recent results from our group retrieved from comparative evolutionary genomic analyses of selected marine animal species will be considered to exemplify how gene novelty and gene enhancement by positive selection might have been determinant in the success of adaptive radiations into diverse habitats and lifestyles.

Keywords: comparative genomics, adaptive evolution, bioinformatics, phylogenetics, genome mining

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Authors: Yaxian Chen, Yeonhee Park

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Driven by evolving FDA recommendations, modern clinical trials demand innovative designs that strike a balance between statistical rigor and ethical considerations. Covariate-adjusted response-adaptive (CARA) designs bridge this gap by utilizing patient attributes and responses to skew treatment allocation in favor of the treatment that is best for an individual patient’s profile. However, existing CARA designs for survival outcomes often hinge on specific parametric models, constraining their applicability in clinical practice. In this article, we address this limitation by introducing a CARA design for survival outcomes (CARAS) based on the Cox model and a variance estimator. This method addresses issues of model misspecification and enhances the flexibility of the design. We also propose a group sequential overlapweighted log-rank test to preserve type I error rate in the context of group sequential trials using extensive simulation studies to demonstrate the clinical benefit, statistical efficiency, and robustness to model misspecification of the proposed method compared to traditional randomized controlled trial designs and response-adaptive randomization designs.

Keywords: cox model, log-rank test, optimal allocation ratio, overlap weight, survival outcome

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Authors: Majid Rezayi, Sh. Shahaboddin, HNM E. Mahmud, A. Yadollah, A. Saeid, A. Yatimah

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In this study, the adaptive neuro-fuzzy inference system (ANFIS) was applied to obtain the membrane composition model affecting the potential response of our reported polymeric PVC sensor for determining the titanium (III) ions. The performance statistics of the artificial neural network (ANN) and linear regression models for potential slope prediction of membrane composition of titanium (III) ion selective electrode were compared with ANFIS technique. The results show that the ANFIS model can be used as a practical tool for obtaining the Nerntian slope of the proposed sensor in this study.

Keywords: adaptive neuro fuzzy inference, PVC sensor, titanium (III) ions, Nerntian slope

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Authors: Brianne Nichols, John A. Parkinson

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Positive Psychology has provided a rich understanding of the beneficial effects of positive emotions in relation to optimal functioning, and research has been devoted to promote states of positive feeling and thinking. While this is a worthwhile pursuit, positive emotions are not useful in all contexts - some situations may require the individual to make use of their negative emotions to reach a desired end state. To account for the potential value of a wider range of emotional experiences that are common to the human condition, Positive Psychology needs to expand its horizons and investigate how individuals achieve positive outcomes using varied means. The current research seeks to understand the positive psychology of fear of failure (FF), which is a commonly experienced negative emotion relevant to most life domains. On the one hand, this emotion has been linked with avoidance motivation and self-handicap behaviours, on the other; FF has been shown to act as a drive to move the individual forward. To fully capture the depth of this highly subjective emotional experience and understand the circumstances under which FF may be adaptive, this study adopted a mixed methods design using SenseMaker; a web-based tool that combines the richness of narratives with the objectivity of numerical data. Two hundred participants consisting mostly of undergraduate university students shared a story of a time in the recent past when they feared failure of achieving a valued goal. To avoid researcher bias in the interpretation of narratives, participants self-signified their stories in a tagging system that was based on researchers’ aim to explore the role of past failures, the cognitive, emotional and behavioural profile of individuals high and low in FF, and the relationship between these factors. In addition, the role of perceived personal control and self-esteem were investigated in relation to FF using self-report questionnaires. Results from quantitative analyses indicated that individuals with high levels of FF, compared to low, were strongly influenced by past failures and preoccupied with their thoughts and emotions relating to the fear. This group also reported an unwillingness to accept their internal experiences, which in turn was associated with withdrawal from goal pursuit. Furthermore, self-esteem was found to mediate the relationship between perceived control and FF, suggesting that self-esteem, with or without control beliefs, may have the potential to buffer against high FF. It is hoped that the insights provided by the current study will inspire future research to explore the ways in which ‘acceptance’ may help individuals keep moving towards a goal despite the presence of FF, and whether cultivating a non-contingent self-esteem is the key to resilience in the face of failures.

Keywords: fear of failure, goal-pursuit, negative emotions, optimal functioning, resilience

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Authors: Ugwu O. C., Mamah R. O., Awudu W. S.

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This work is aimed at enhancing signal reception on a mobile radio network and minimizing outage probability in a mobile radio network using adaptive beamforming antenna arrays. In this research work, an empirical real-time drive measurement was done in a cellular network of Globalcom Nigeria Limited located at Ikeja, the headquarters of Lagos State, Nigeria, with reference base station number KJA 004. The empirical measurement includes Received Signal Strength and Bit Error Rate which were recorded for exact prediction of the signal strength of the network as at the time of carrying out this research work. The Received Signal Strength and Bit Error Rate were measured with a spectrum monitoring Van with the help of a Ray Tracer at an interval of 100 meters up to 700 meters from the transmitting base station. The distance and angular location measurements from the reference network were done with the help Global Positioning System (GPS). The other equipment used were transmitting equipment measurements software (Temsoftware), Laptops and log files, which showed received signal strength with distance from the base station. Results obtained were about 11% from the real-time experiment, which showed that mobile radio networks are prone to signal failure and can be minimized using an Adaptive Beamforming Antenna Array in terms of a significant reduction in Bit Error Rate, which implies improved performance of the mobile radio network. In addition, this work did not only include experiments done through empirical measurement but also enhanced mathematical models that were developed and implemented as a reference model for accurate prediction. The proposed signal models were based on the analysis of continuous time and discrete space, and some other assumptions. These developed (proposed) enhanced models were validated using MATLAB (version 7.6.3.35) program and compared with the conventional antenna for accuracy. These outage models were used to manage the blocked call experience in the mobile radio network. 20% improvement was obtained when the adaptive beamforming antenna arrays were implemented on the wireless mobile radio network.

Keywords: beamforming algorithm, adaptive beamforming, simulink, reception

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Authors: Valeria Abreu Minero, Edward Barker, Hannah Dickson, Francois Husson, Sandra Flynn, Jennifer Shaw

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Purpose – The purpose of this paper is to identify offender typologies based on aspects of the offenders’ psychopathology and their associations with crime scene behaviours using data derived from the National Confidential Enquiry into Suicide and Safety in Mental Health concerning homicides in England and Wales committed by offenders in contact with mental health services in the year preceding the offence (n=759). Design/methodology/approach – The authors used multiple correspondence analysis to investigate the interrelationships between the variables and hierarchical agglomerative clustering to identify offender typologies. Variables describing: the offender’s mental health history; the offenders’ mental state at the time of offence; characteristics useful for police investigations; and patterns of crime scene behaviours were included. Findings – Results showed differences in the offender’s histories in relation to their crime scene behaviours. Further, analyses revealed three homicide typologies: externalising, psychosis and depression. Analyses revealed three homicide typologies: externalising, psychotic and depressive. Practical implications – These typologies may assist the police during homicide investigations by: furthering their understanding of the crime or likely suspect; offering insights into crime patterns; provide advice as to what an offender’s offence behaviour might signify about his/her mental health background; findings suggest information concerning offender psychopathology may be useful for offender profiling purposes in cases of homicide offenders with schizophrenia, depression and comorbid diagnosis of personality disorder and alcohol/drug dependence. Originality/value – Empirical studies with an emphasis on offender profiling have almost exclusively focussed on the inference of offender demographic characteristics. This study provides a first step in the exploration of offender psychopathology and its integration to the multivariate analysis of offence information for the purposes of investigative profiling of homicide by identifying the dominant patterns of mental illness within homicidal behaviour.

Keywords: offender profiling, mental illness, psychopathology, multivariate analysis, homicide, crime scene analysis, crime scene behviours, investigative advice

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Authors: Sirus Mohammadi, Rohala Moghimi

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The main advantages of microgrids are high energy efficiency through the application of Combined Heat and Power (CHP), high quality and reliability of the delivered electric energy and environmental and economic advantages. This study presents an energy management system (EMS) to optimize the operation of the microgrid (MG). In this paper an Adaptive Modified Firefly Algorithm (AMFA) is presented for optimal operation of a typical MG with renewable energy sources (RESs) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the energy surplus when it’s needed. The problem is formulated as a nonlinear constraint problem to minimize the total operating cost. The management of Energy storage system (ESS), economic load dispatch and operation optimization of distributed generation (DG) are simplified into a single-object optimization problem in the EMS. The proposed algorithm is tested on a typical grid-connected MG including WT/PV/Micro Turbine/Fuel Cell and Energy Storage Devices (ESDs) then its superior performance is compared with those from other evolutionary algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Self Adaptive PSO (FSAPSO), Chaotic Particle PSO (CPSO), Adaptive Modified PSO (AMPSO), and Firefly Algorithm (FA).

Keywords: microgrid, operation management, optimization, firefly algorithm (AMFA)

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Authors: Anita Bela, Marta Juhasz

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Adapting to the always-changing environment comes with complex determinants. The authors are zooming into one aspect only when the current workforce comes with obstacles by being less keen to stay engaged, even short or mid-term, resulting in additional challenges impacting the business performance. Seeing these occurring in practice made the researchers eager to gain a better understanding of the reasons behind. The paper aims to provide an overview of the theoretical background and research methods planned for the different stages of the research. The theoretical part takes the leadership behaviors under lens while the focus is on finding ways to attract and retain those who prefer working under more flexible employment conditions (e.g. contractor, contingent worker, etc.). These are considered as the organizational values and along with the power of people management are having their engaging relevance. The organizational culture (visible or invisible level) is clearly the mirror of the set of shared values guiding all members of the companies towards acceptable behavior. The applied research method, inductive reasoning was selected since the focus and questions raised in this research are results of specific observations made on the employees (various employment types) and leaders of start-ups and corporates. By comparing the similarities and differences, the researchers are hoping to prove the readiness and agility of the start-up culture for the desired leadership behaviours of the current and future workforce against the corporate culture. While exploring the preferences and engaging factors of the 21st-century workforce the data gathering would happen through website analysis – using ATLAS.ti qualitative software – followed by interview sessions where demographics will be collected and preferred leadership behaviors - using the Critical Incident Technique. Moreover, a short engagement survey will be administered to understand the linkage between the organizational culture type and engagement level. To conclude, after gaining theoretical understanding, we will zoom back to the employees to reveal the behaviors to be followed to achieve engagement in an environment where nothing is stable and where the companies always must keep their agile eyes and reactions vivid.

Keywords: leadership behaviours, organizational culture, qualitative analysis, workforce engagement

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Authors: Hammoudi Abderazek

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The present paper introduces a modified adaptive mixed differential evolution (MAMDE) to select the main geometry parameters of specific cylindrical spur gear. The developed algorithm used the self-adaptive mechanism in order to update the values of mutation and crossover factors. The feasibility rules are used in the selection phase to improve the search exploration of MAMDE. Moreover, the elitism is performed to keep the best individual found in each generation. For the constraints handling the normalization method is used to treat each constraint design equally. The finite element analysis is used to confirm the optimization results for the maximum bending resistance. The simulation results reached in this paper indicate clearly that the proposed algorithm is very competitive in precision gear design optimization.

Keywords: evolutionary algorithm, spur gear, tooth profile, meta-heuristics

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Authors: Stefan Kurbatfinski, Aliyah Dosani, Andrew F. Hayes, Deborah Dewey, Nicole Letourneau

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Background: Maternal adverse childhood experiences (ACEs) have been associated with internalizing and externalizing behavioral problems in preschool children. However, little is known about the influence of maternal adult attachment patterns on this association. Further, potential moderation by maternal mental health, maternal social support, or child sex is poorly understood. Therefore, this study examined associations between 1) maternal ACEs and preschool children’s behavioural problems, with mediation through maternal attachment patterns and moderation by maternal mental health, maternal social support, and child sex; and 2) maternal attachment patterns and children’s behavioural problems, with mediation through maternal mental health and social support and moderation by child sex. Methods: This secondary analysis used data (n=625) from a high socioeconomic, longitudinally prospective cohort (Alberta Pregnancy Outcomes and Nutrition). Child behaviour (BASC-2) and maternal adult attachment (ECR-Q) were measured at five years of child age, maternal ACEs (ACEs Questionnaire) at around 12 months, and maternal mental health (CESD and SCL-90-R) and social support (SSQ) across various prenatal and postnatal time points. All moderation and mediation analyses occurred through RStudio using PROCESS, interpreting significant interactions through Johnson-Neyman plots. Findings: Maternal ACEs interacted with maternal anxiety symptoms to predict both behavioural problems (pexternalizing=0.007; pinternalizing=0.0159). An indirect pathway via dismissive attachment was moderated by maternal social support ([0.0058, 0.0596]). Attachment patterns predicted all behavioural problems (p<0.05) and interacted with maternal anxiety symptoms to predict internalizing behaviours among male children ([0.0321, 0.1307]; [0.0321, 0.1291]). Interpretation: Maternal attachment patterns may predict children’s behavioural problems more than ACEs. Social support interventions may not always be beneficial for highly dismissively attached mothers. Implications for policy and child health include mandatory sex and gender education for teachers; assessing attachment patterns prior to recommending social support as an intervention; and anxiety-focused interventions for mothers in higher socioeconomic populations.

Keywords: maternal adverse childhood experiences, internalizing behaviours, externalizing behaviours, mediators and moderators, attachment patterns, child health

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Authors: A. Kamel Abd Elaziz Mohamed, B. Sameh Kamal el Maraghi

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Introduction: Adaptive support ventilation (ASV) is an improved closed-loop ventilation mode that provides both pressure-controlled ventilation and PSV according to the patient’s needs. Aim of the work: To compare the short-term effects of Adaptive support ventilation (ASV), with conventional Pressure support ventilation (PSV) in weaning of intubated COPD patients. Patients and methods: Fifty patients admitted in the intensive care with acute exacerbation of COPD and needing intubation were included in the study. All patients were initially ventilated with control/assist control mode, in a stepwise manner and were receiving standard medical therapy. Patients were randomized into two groups to receive either ASV or PSV. Results: Out of fifty patients included in the study forty one patients in both studied groups were weaned successfully according to their ABG data and weaning indices. APACHE II score showed no significant difference in both groups. There were statistically significant differences between the groups in term of, duration of mechanical ventilation, weaning hours and length of ICU stay being shorter in (group 1) weaned by ASV. Re-intubation and mortality rate were higher in (group 11) weaned by conventional PSV, however the differences were not significant. Conclusion: ASV can provide automated weaning and achieve shorter weaning time for COPD patients hence leading to reduction in the total duration of MV, length of stay, and hospital costs.

Keywords: COPD patients, ASV, PSV, mechanical ventilation (MV)

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Authors: Kyoung-Jin Joo, In-Gun Kim, Hyun-Seok Hong, Dong-Woo Kang, Ju Lee

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Recently, the permanent magnet-assisted synchronous reluctance motor (PMa-SynRM) that can be substituted for the induction motor has been studying because of the needs of the development of the premium high efficiency motor for the minimum energy performance standard (MEPS). PMa-SynRM is required to the speed and position information for motor speed and torque controls. However, to apply the sensors has many problems that are sensor mounting space shortage and additional cost, etc. Therefore, in this paper, speed-sensorless control based on model reference adaptive system (MRAS) is introduced to eliminate the sensor. The sensorless method is constructed in a reference model as standard and an adaptive model as the state observer. The proposed algorithm is verified by the simulation.

Keywords: PMa-SynRM, sensorless control, robust estimation, MRAS method

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Authors: Stephanie Yun Yu Law

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Background: This study aims to investigate into communication between trainee doctors and patients, especially how doctor’s reaction to patient’s emotional issues expressed in the consultation affect patient’s satisfaction. Objectives: Thus, there are three aims in this study, 1.) how do trainee doctors react to patients emotional cues in OSCE station? 2.) Any differences in the respond type to emotional cues between first year students and third year students? 3.) Is response type (reducing space) related to OSCE outcome (patient satisfaction and expert rating)? Methods: Fifteen OSCE stations was videotaped, in which 9 were stations with first-year students and 6 were with third-year students. OSCE outcomes were measured by Communication Assessment Tool and Examiners Checklist. Analyses: All patient’s cues/concerns and student’s reaction were coded by Verona Coding Definitions of Emotional Sequence. Descriptive data was gathered from Observer XT and logistic regression (two-level) was carried out to see if occurrence of reducing space response can be predicted by OSCE outcomes. Results: Reducing space responses from all students were slightly less than a half in total responses to patient’s cues. The mean percentage of reducing space behaviours was lower among first year students when compared to third year students. Patient’s satisfaction significantly (p<0.05) and negatively predicted reducing space behaviours. Conclusions: Most of the medical students, to some extent, did not provide adequate responses for patient’s emotional cues. But first year students did provide more space for patients to talk about their emotional issues when compared to third year students. Lastly, patients would feel less satisfied if trainee doctors use more reducing space responses in reaction to patient’s expressed emotional cues/concerns. Practical implications: Firstly, medical training programme can be tailored on teaching students how to detect and respond appropriately to emotional cues in order to improve underperformed student’s communication skills in healthcare setting. Furthermore, trainee doctor’s relationship with patients in clinical practice can also be improved by reacting appropriately to patient’s emotive cues in consultations (such as limit the use of reducing space behaviours).

Keywords: doctors-patients communication, applied clinical psychology, health psychology, healthcare professionals

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Authors: Chien-kuo Chang, Bo-wei Wu, Yi-yun Tang, Min-chiu Wu

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This paper proposes a power equipment diagnosis system based on partial discharge (PD), which is characterized by increasing the readability of experimental data and the convenience of operation. This system integrates a variety of analysis programs of different data formats and different programming languages and then establishes a set of interfaces that can follow and expand the structure, which is also helpful for subsequent maintenance and innovation. This study shows a case of using the developed Convolutional Neural Networks (CNN) to integrate with this system, using the designed model architecture to simplify the complex training process. It is expected that the simplified training process can be used to establish an adaptive deep learning experimental structure. By selecting different test data for repeated training, the accuracy of the identification system can be enhanced. On this platform, the measurement status and partial discharge pattern of each equipment can be checked in real time, and the function of real-time identification can be set, and various training models can be used to carry out real-time partial discharge insulation defect identification and insulation state diagnosis. When the electric power equipment entering the dangerous period, replace equipment early to avoid unexpected electrical accidents.

Keywords: partial discharge, convolutional neural network, partial discharge analysis platform, adaptive deep learning

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Authors: Priyesh Kumar Pandey, Zakir Husain, R. K. Jarial

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Condition monitoring and diagnostic is important for testing of power transformer in order to estimate the remnant life. Concentration of furan content in transformer oil can be a promising indirect measurement of the aging of transformer insulation. The oil gets contaminated mainly due to ageing. The present paper introduces adaptive neuro fuzzy technique to correlate furanic compounds obtained by high performance liquid chromatography (HPLC) test and remnant life of the power transformer. The results are obtained by conducting HPLC test at TIFAC-CORE lab, NIT Hamirpur on thirteen power transformer oil samples taken from Himachal State Electricity Board, India.

Keywords: adaptive neuro fuzzy technique, furan compounds, remnant life, transformer oil

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Authors: Gurmail Singh

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Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

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Authors: Jing Zhao, Dayong Liu, Shihao Wang, Xinghua Zhu, Delong Li

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Uhumanned Underwater Vehicles generally operate in the deep sea, which has its own unique working conditions. Lithium-ion power batteries should have the necessary stability and endurance for use as an underwater vehicle’s power source. Therefore, it is essential to accurately forecast how long lithium-ion batteries will last in order to maintain the system’s reliability and safety. In order to model and forecast lithium battery Remaining Useful Life (RUL), this research suggests a model based on Complete Ensemble Empirical Mode Decomposition with Adaptive noise-Temporal Convolutional Net (CEEMDAN-TCN). In this study, two datasets, NASA and CALCE, which have a specific gap in capacity data fluctuation, are used to verify the model and examine the experimental results in order to demonstrate the generalizability of the concept. The experiments demonstrate the network structure’s strong universality and ability to achieve good fitting outcomes on the test set for various battery dataset types. The evaluation metrics reveal that the CEEMDAN-TCN prediction performance of TCN is 25% to 35% better than that of a single neural network, proving that feature expansion and modal decomposition can both enhance the model’s generalizability and be extremely useful in industrial settings.

Keywords: lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

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Authors: Bastien Batardière, Joon Kwon

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For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster convergence than SGD thanks to a carefully crafted lower-variance stochastic gradient estimator that reuses past gradients. Another important line of research of the past decade in continuous optimization is the adaptive algorithms such as AdaGrad, that dynamically adjust the (possibly coordinate-wise) learning rate to past gradients and thereby adapt to the geometry of the objective function. Variants such as RMSprop and Adam demonstrate outstanding practical performance that have contributed to the success of deep learning. In this work, we present AdaLVR, which combines the AdaGrad algorithm with loopless variance-reduced gradient estimators such as SAGA or L-SVRG that benefits from a straightforward construction and a streamlined analysis. We assess that AdaLVR inherits both good convergence properties from VR methods and the adaptive nature of AdaGrad: in the case of L-smooth convex functions we establish a gradient complexity of O(n + (L + √ nL)/ε) without prior knowledge of L. Numerical experiments demonstrate the superiority of AdaLVR over state-of-the-art methods. Moreover, we empirically show that the RMSprop and Adam algorithm combined with variance-reduced gradients estimators achieve even faster convergence.

Keywords: convex optimization, variance reduction, adaptive algorithms, loopless

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Authors: Masao Okabe

Abstract:

Nowadays, in Japan, variety of students get into a university and one of the main roles of introductory courses for freshmen is to make such students well prepared for subsequent intermediate courses. For that purpose, the flipped-mastery model is not enough because videos usually used in a flipped classroom is not adaptive and does not fit all freshmen with different academic performances. This paper proposes an ontology-navigated tutoring system called EduGraph. Using EduGraph, students can prepare for and review a class, in a more flexibly personalizable way than by videos. Structuralizing learning materials by its ontology, EduGraph also helps students integrate what they learn as knowledge, and makes learning materials sharable. EduGraph was used for an introductory course for freshmen. This application suggests that EduGraph is effective.

Keywords: adaptive e-learning, flipped classroom, mastery learning, ontology

Procedia PDF Downloads 271

Authors: Minh-Thuan Nguyen, Phuong-Thai Nguyen, Van-Vinh Nguyen, Quang-Minh Nguyen

Abstract:

Research on automatically generating descriptions for e-commerce products is gaining increasing attention in recent years. However, the generated descriptions of their systems are often less informative and attractive because of lacking training datasets or the limitation of these approaches, which often use templates or statistical methods. In this paper, we explore a method to generate production descriptions by using the GPT-2 model. In addition, we apply text paraphrasing and task-adaptive pretraining techniques to improve the qualify of descriptions generated from the GPT-2 model. Experiment results show that our models outperform the baseline model through automatic evaluation and human evaluation. Especially, our methods achieve a promising result not only on the seen test set but also in the unseen test set.

Keywords: GPT-2, product description, transformer, task-adaptive, language model, pretraining

Procedia PDF Downloads 189

Authors: Thuvanan Borvornvitchotikarn, Werasak Kurutach

Abstract:

Mutual information (MI) is widely used in medical image registration. In the different medical images analysis, it is difficult to choose an optimal bins size number for calculating the probability distributions in MI. As the result, this paper presents a new adaptive bins number selection approach that named a hybrid EMPCA-Scott approach. This work combines an expectation maximization principal component analysis (EMPCA) and the modified Scott’s rule. The proposed approach solves the binning problem from the various intensity values in medical images. Experimental results of this work show the lower registration errors compared to other adaptive binning approaches.

Keywords: mutual information, EMPCA, Scott, probability distributions

Procedia PDF Downloads 242

Authors: Sana El-Azzeh, Farraj Al-Ajmi, Abdulrahman Al-Aqqad, Mohamed Salem

Abstract:

Thermal comfort is defined according to ANSI/ASHRAE Standard 55 as a condition of mind that expresses satisfaction with the thermal environment and is assessed by subjective evaluation. Sustaining this standard of thermal comfort for occupants of buildings or other enclosures is one of the important goals of HVAC design engineers. This paper presents a study of thermal comfort and adaptive behaviors of occupants who occupies two locations at the campus of the Australian College of Kuwait. A longitudinal survey and field measurement were conducted to measure thermal comfort, adaptive behaviors, and indoor environment qualities. The study revealed that female occupants in the selected locations felt warmer than males and needed more air velocity and lower temperature.

Keywords: indoor thermal comfort, educational facility, gender analysis, dry desert climate

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Authors: José Carlos Rodríguez, Mario Gómez, Medardo Serna

Abstract:

This paper analyses managing higher education institutions in emerging economies. The paper investigates the case of postgraduate studies development at public universities. In so doing, it adopts the complex theory approach to evaluate how postgraduate studies have evolved in these countries. The investigation suggests that the postgraduate studies sector at public universities can be seen as a complex adaptive system (CAS). Therefore, the paper adopts system dynamics (SD) methods to develop this analysis. The case of postgraduate studies at Universidad Michoacana de San Nicolás de Hidalgo in Mexico is investigated in this paper.

Keywords: complex adaptive systems, higher education institutions, Mexico, system dynamics

Procedia PDF Downloads 302

Authors: Zixuan Xu, Yichun Lou, Yang Song, Zihuai Lin

Abstract:

MediaPipe is an open-source machine learning computer vision framework that can be ported into a multi-platform environment, which makes it easier to use it to recognize the human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition.

Keywords: computer vision, MediaPipe, adaptive boosting, fast dynamic time warping

Procedia PDF Downloads 98