Search results for: adaptive computer games

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: Marlene Rosa, Susana Lopes

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There are few studies that explore the potential of board games as a performance measure, despite it can be an interesting strategy in the context of frailty populations. In fact, board games are immersive strategies than can inhibit the pressure of being evaluated. This study aimed to test the ability of gamed-base strategies to assess executive function in elderly population. Sixteen old participants were included: 10 with affected executive functions (G1 – 85.30±6.00 yrs old; 10 male); 6 with executive functions with non-clinical important modifications (G2 - 76.30±5.19 yrs old; 6 male). Executive tests were assessed using the Frontal Assessment Battery (FAB), which is a quick-applicable cognitive screening test (score<12 means impairment). The board game used in this study was the TATI Hand Game, specifically for training rhythmic coordination of the upper limbs with multiple cognitive stimuli. This game features 1 table grid, 1 set of Single Game cards (to play with one hand); Double Game cards (to play simultaneously with two hands); 1 dice to plan Single Game mode; cards to plan the Double Game mode; 1 bell; 2 cups. Each participant played 3 single game cards, and the following data were collected: (i) variability in time during board game challenges (SD); (ii) number of errors; (iii) execution speed (sec). G1 demonstrated: high variability in execution time during board game challenges (G1 – 13.0s vs G2- 0.5s); a higher number of errors (1.40 vs 0.67); higher execution velocity (607.80s vs 281.83s). These results demonstrated the potential of implementing board games as a functional assessment strategy in geriatric care. Future studies might include larger samples and statistical methodologies to find cut-off values for impairment in executive functions during performance in TATI game.

Keywords: board game, aging, executive function, evaluation

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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: Bayar Shahab

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The fast development of technology that has advanced neuroscience and human interaction with computers has enabled solutions to various problems, and issues of this new era have been found and are being found like no other time in history. Brain-computer interface so-called BCI has opened the door to several new research areas and have been able to provide solutions to critical and important issues such as supporting a paralyzed patient to interact with the outside world, controlling a robot arm, playing games in VR with the brain, driving a wheelchair or even a car and neurotechnology enabled the rehabilitation of the lost memory, etc. This review work presents state-of-the-art methods and improvements of canonical correlation analyses (CCA), which is an SSVEP-based BCI method. These are the methods used to extract EEG signal features or, to be said in a different way, the features of interest that we are looking for in the EEG analyses. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers to understand the most state-of-the-art methods available in this field with their pros and cons, along with their mathematical representations and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) stating most of the prominent methods used in this field in a hierarchical way (2) explaining pros and cons of each method and their performance (3) presenting the gaps that exist at the end of each method that can open the understanding and doors to new research and/or improvements.

Keywords: BCI, CCA, SSVEP, EEG

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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: Tobias Huwer, Thomas Bobek, Gunter Spöcker

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Driving forces for enhancements in production are trends like digitalization and individualized production. Currently, such developments are restricted to assembly parts. Thus, complex freeform surfaces are not addressed in this context. The need for efficient use of resources and near-net-shape production will require individualized production of complex shaped workpieces. Due to variations between nominal model and actual geometry, this can lead to changes in operations in Computer-aided process planning (CAPP) to make CAPP manageable for an adaptive serial production. In this context, 3D CAD data can be a key to realizing that objective. Along with developments in the geometrical adaptation, a preceding inspection method based on CAD data is required to support the process planner by finding objective criteria to make decisions about the adaptive manufacturability of workpieces. Nowadays, this kind of decisions is depending on the experience-based knowledge of humans (e.g. process planners) and results in subjective decisions – leading to a variability of workpiece quality and potential failure in production. In this paper, we present an automatic part inspection method, based on design and measurement data, which evaluates actual geometries of single workpiece preforms. The aim is to automatically determine the suitability of the current shape for further machining, and to provide a basis for an objective decision about subsequent adaptive manufacturability. The proposed method is realized by a workflow-based approach, keeping in mind the requirements of industrial applications. Workflows are a well-known design method of standardized processes. Especially in applications like aerospace industry standardization and certification of processes are an important aspect. Function blocks, providing a standardized, event-driven abstraction to algorithms and data exchange, will be used for modeling and execution of inspection workflows. Each analysis step of the inspection, such as positioning of measurement data or checking of geometrical criteria, will be carried out by function blocks. One advantage of this approach is its flexibility to design workflows and to adapt algorithms specific to the application domain. In general, within the specified tolerance range it will be checked if a geometrical adaption is possible. The development of particular function blocks is predicated on workpiece specific information e.g. design data. Furthermore, for different product lifecycle phases, appropriate logics and decision criteria have to be considered. For example, tolerances for geometric deviations are different in type and size for new-part production compared to repair processes. In addition to function blocks, appropriate referencing systems are important. They need to support exact determination of position and orientation of the actual geometries to provide a basis for precise analysis. The presented approach provides an inspection methodology for adaptive and part-individual process chains. The analysis of each workpiece results in an inspection protocol and an objective decision about further manufacturability. A representative application domain is the product lifecycle of turbine blades containing a new-part production and a maintenance process. In both cases, a geometrical adaptation is required to calculate individual production data. In contrast to existing approaches, the proposed initial inspection method provides information to decide between different potential adaptive machining processes.

Keywords: adaptive, CAx, function blocks, turbomachinery

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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: H. Varol

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Although students’ interest level in pursuing Computer Science and related degrees are lower than previous decade, fundamentals of computers, specifically introductory level programming courses are either listed as core or elective courses for a number of non-computer science majors. Universities accommodate these non-computer science majored students either via creating separate sections of a class for them or simply offering mixed-body classroom solutions, in which both computer science and non-computer science students take the courses together. In this work, we demonstrated how we handle introductory level programming course at Sam Houston State University and also provide facts about our observations on students’ success during the coursework. Moreover, we provide suggestions and methodologies that are based on students’ major and skills to overcome the deficiencies of mix-body type of classes.

Keywords: computer science, non-computer science major, programming, programming education

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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: Garry Gorman, Nigel McKelvey, James Connolly

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This study will evaluate a growth mindset intervention with Junior Cycle Coding and Senior Cycle Computer Science students in Ireland, where gamification will be used to incentivise growth mindset behaviour. An artificial intelligence (AI) driven personalised learning system will be developed to present computer programming learning tasks in a manner that is best suited to the individuals’ own learning preferences while incentivising and rewarding growth mindset behaviour of persistence, mastery response to challenge, and challenge seeking. This research endeavours to measure mindset with before and after surveys (conducted nationally) and by recording growth mindset behaviour whilst playing a digital game. This study will harness the capabilities of AI and aims to determine how a personalised learning (PL) experience can impact the mindset of a broad range of students. The focus of this study will be to determine how personalising the learning experience influences female and disadvantaged students' sense of belonging in the computer science classroom when tasks are presented in a manner that is best suited to the individual. Whole Brain Learning will underpin this research and will be used as a framework to guide the research in identifying key areas such as thinking and learning styles, cognitive potential, motivators and fears, and emotional intelligence. This research will be conducted in multiple school types over one academic year. Digital games will be played multiple times over this period, and the data gathered will be used to inform the AI algorithm. The three data sets are described as follows: (i) Before and after survey data to determine the grit scores and mindsets of the participants, (ii) The Growth Mind-Set data from the game, which will measure multiple growth mindset behaviours, such as persistence, response to challenge and use of strategy, (iii) The AI data to guide PL. This study will highlight the effectiveness of an AI-driven personalised learning experience. The data will position AI within the Irish educational landscape, with a specific focus on the teaching of CS. These findings will benefit coding and computer science teachers by providing a clear pedagogy for the effective delivery of personalised learning strategies for computer science education. This pedagogy will help prevent students from developing a fixed mindset while helping pupils to exhibit persistence of effort, use of strategy, and a mastery response to challenges.

Keywords: computer science education, artificial intelligence, growth mindset, pedagogy

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Authors: Nuno Biga

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The integration of serious games with gamification in management education and training has gained significant importance in recent years as innovative strategies are sought to improve target audience engagement and learning outcomes. This research builds on the author's previous work in this field and presents a case study that evaluates the ex-post impact of a sample of applications of the BIGAMES management simulator in the training of top managers from various hospital institutions. The methodology includes evaluating the reaction of participants after each edition of BIGAMES Accident & Emergency (A&E) carried out over the last 3 years, as well as monitoring the career path of a significant sample of participants and their feedback more than a year after their experience with this simulator. Control groups will be set up, according to the type of role their members held when they took part in the BIGAMES A&E simulator: Administrators, Clinical Directors and Nursing Directors. Former participants are invited to answer a questionnaire structured for this purpose, where they are asked, among other questions, about the importance and impact that the BIGAMES A&E simulator has had on their professional activity. The research methodology also includes an exhaustive literature review, focusing on empirical studies in the field of education and training in management and business that investigate the effectiveness of gamification and serious games in improving learning, team collaboration, critical thinking, problem-solving skills and overall performance, with a focus on training contexts in the health sector. The results of the research carried out show that gamification and serious games that simulate real scenarios, such as Business Interactive Games - BIGAMES©, can significantly increase the motivation and commitment of participants, stimulating the development of transversal skills, the mobilization of group synergies and the acquisition and retention of knowledge through interactive user-centred scenarios. Individuals who participate in game-based learning series show a higher level of commitment to learning because they find these teaching methods more enjoyable and interactive. This research study aims to demonstrate that, as executive education and training programs develop to meet the current needs of managers, gamification and serious games stand out as effective means of bridging the gap between traditional teaching methods and modern educational and training requirements. To this end, this research evaluates the medium/long-term effects of gamified learning on the professional performance of participants in the BIGAMES simulator applied to healthcare. Based on the conclusions of the evaluation of the effectiveness of training using gamification and taking into account the results of the opinion poll of former A&E participants, this research study proposes an integrated approach for the transversal application of the A&E Serious Game in various educational contexts, covering top management (traditionally the target audience of BIGAMES A&E), middle and operational management in healthcare institutions (functional area heads and professionals with career development potential), as well as higher education in medicine and nursing courses. The integrated solution called “BIGAMES A&E plus”, developed as part of this research, includes the digitalization of key processes and the incorporation of AI.

Keywords: artificial intelligence (AI), executive training, gamification, higher education, management simulators, serious games (SG), training effectiveness

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Authors: Witthaya Mekhum, Wutthikorn Malikong

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The objective of this study is to investigate the increasing desktop computer performance after overclocking central processing unit or CPU by running a computer component at a higher clock rate (more clock cycles per second) than it was designed at the rate of 0.1 GHz for each level or 100 MHz starting at 4000 GHz-4500 GHz. The computer performance is tested for each level with 4 programs, i.e. Hyper PI ver. 0.99b, Cinebench R15, LinX ver.0.6.4 and WinRAR . After the CPU overclock, the computer performance increased. When overclocking CPU at 29% the computer performance tested by Hyper PI ver. 0.99b increased by 10.03% and when tested by Cinebench R15 the performance increased by 20.05% and when tested by LinX Program the performance increased by 16.61%. However, the performance increased only 8.14% when tested with Winrar program. The computer performance did not increase according to the overclock rate because the computer consists of many components such as Random Access Memory or RAM, Hard disk Drive, Motherboard and Display Card, etc.

Keywords: overclock, performance, central processing unit, computer

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Authors: Roberta Gentry

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The presentation will begin with a brief literature review of the use of computer simulation in teacher education programs. This information will be summarized. Additionally, based on the literature review, advantages and disadvantages of using computer simulation in higher education will be shared. Finally, a study in which computer simulations software was used with 50 initial licensure teacher candidates in both an introductory course and a behavior management course will be shared. Candidates reflected on their experiences with using computer simulation. The instructor of the course will also share lessons learned.

Keywords: simulations, teacher education, teacher preparation, educational research

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Authors: N. Ismail, O. Thammajinda, U. Thongpanya

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Games-based learning (GBL) has become increasingly important in teaching and learning. This paper explains the first two phases (analysis and design) of a GBL development project, ending up with a prototype design based on students’ and teachers’ perceptions. The two phases are part of a full cycle GBL project aiming to help secondary school students in Thailand in their study of Comprehensive Sex Education (CSE). In the course of the study, we invited 1,152 students to complete questionnaires and interviewed 12 secondary school teachers in focus groups. This paper found that GBL can serve students in their learning about CSE, enabling them to gain understanding of their sexuality, develop skills, including critical thinking skills and interact with others (peers, teachers, etc.) in a safe environment. The objectives of this paper are to outline the development of GBL variables from the research question(s) into the developers’ flow chart, to be responsive to the GBL beneficiaries’ preferences and expectations, and to help in answering the research questions. This paper details the steps applied to generate GBL variables that can feed into a game flow chart to develop a GBL prototype. In our approach, we detailed two models: (1) Game Elements Model (GEM) and (2) Game Object Model (GOM). There are three outcomes of this research – first, to achieve the objectives and benefits of GBL in learning, game design has to start with the research question(s) and the challenges to be resolved as research outcomes. Second, aligning the educational aims with engaging GBL end users (students) within the data collection phase to inform the game prototype with the game variables is essential to address the answer/solution to the research question(s). Third, for efficient GBL to bridge the gap between pedagogy and technology and in order to answer the research questions via technology (i.e. GBL) and to minimise the isolation between the pedagogists “P” and technologist “T”, several meetings and discussions need to take place within the team.

Keywords: games-based learning, engagement, pedagogy, preferences, prototype

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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: Yong-Heng Shi, Peng Hao, Bai-Chen Xie

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Solar prosumers are playing increasingly prominent roles in the power system. However, its uncertainty affects the outcomes and functions of the power market, especially in the asymmetric information environment. Therefore, an important issue is how to take effective measures to reduce the impact of uncertainty on market equilibrium. We propose a two-level stochastic differential game model to explore the Cournot decision problem of prosumers. In particular, we study the impact of punishment and cooperation mechanisms on the efficiency of the Cournot game in which prosumers face uncertainty. The results show that under the penalty mechanism of fixed and variable rates, producers and consumers tend to take conservative actions to hedge risks, and the variable rates mechanism is more reasonable. Compared with non-cooperative situations, prosumers can improve the efficiency of the game through cooperation, which we attribute to the superposition of market power and uncertainty reduction. In addition, the market environment of asymmetric information intensifies the role of uncertainty. It reduces social welfare but increases the income of prosumers. For regulators, promoting alliances is an effective measure to realize the integration, optimization, and stable grid connection of producers and consumers.

Keywords: Cournot games, power market, uncertainty, prosumer cooperation

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Authors: Agni Stylianou-Georgiou, Eliza Pitri

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This study is a design narrative that discusses researchers’ new learning based on changes made in pedagogies and learning opportunities in the context of a Cognitive Psychology and an Art History undergraduate course. The purpose of this study was to investigate how to encourage creative problem-based play in tertiary education engaging instructors and student-teachers in designing educational games. Course instructors modified content to encourage flexible thinking during game design problem-solving. Qualitative analyses of data sources indicated that Thinking Birds’ questions could encourage flexible thinking as instructors engaged in creative problem-based play. However, student-teachers demonstrated weakness in adopting flexible thinking during game design problem solving. Further studies of student-teachers’ shifting perspectives during different instructional design tasks would provide insights for developing the Thinking Birds’ questions as tools for creative problem solving.

Keywords: creative problem-based play, educational games, flexible thinking, tertiary education

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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: Esther Ekundayo

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The nature of AI-generated works makes it difficult to identify an author. Although, some scholars have suggested that all the players involved in its creation should be allocated authorship according to their respective contribution. From the programmer who creates and designs the AI to the investor who finances the AI and to the user of the AI who most likely ends up creating the work in question. While others suggested that this issue may be resolved by the UK computer-generated works (CGW) provision under Section 9(3) of the Copyright Designs and Patents Act 1988. However, under the UK and Nigerian copyright law, only human-created works are recognised. This is usually assessed based on their originality. This simply means that the work must have been created as a result of its author’s creative and intellectual abilities and not copied. Such works are literary, dramatic, musical and artistic works and are those that have recently been a topic of discussion with regards to generative artificial intelligence (Generative AI). Unlike Nigeria, the UK CDPA recognises computer-generated works and vests its authorship with the human who made the necessary arrangement for its creation . However, making necessary arrangement in the case of Nova Productions Ltd v Mazooma Games Ltd was interpreted similarly to the traditional authorship principle, which requires the skills of the creator to prove originality. Although, some recommend that computer-generated works complicates this issue, and AI-generated works should enter the public domain as authorship cannot be allocated to AI itself. Additionally, the UKIPO recognising these issues in line with the growing AI trend in a public consultation launched in the year 2022, considered whether computer-generated works should be protected at all and why. If not, whether a new right with a different scope and term of protection should be introduced. However, it concluded that the issue of computer-generated works would be revisited as AI was still in its early stages. Conversely, due to the recent developments in this area with regards to Generative AI systems such as ChatGPT, Midjourney, DALL-E and AIVA, amongst others, which can produce human-like copyright creations, it is therefore important to examine the relevant issues which have the possibility of altering traditional copyright principles as we know it. Considering that the UK and Nigeria are both common law jurisdictions but with slightly differing approaches to this area, this research, therefore, seeks to answer the following questions by comparative analysis: 1)Who is the author of an AI-generated work? 2)Is the UK’s CGW provision worthy of emulation by the Nigerian law? 3) Would a sui generis law be capable of protecting AI-generated works and its author under both jurisdictions? This research further examines the possible barriers to the implementation of the new law in Nigeria, such as limited technical expertise and lack of awareness by the policymakers, amongst others.

Keywords: authorship, artificial intelligence (AI), generative ai, computer-generated works, copyright, technology

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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: Mario A. Maggioni, Domenico Rossignoli, Simona Beretta, Sara Balestri

Abstract:

The paper presents the results of a Longitudinal Randomized Control Trial implemented in 2016 two State Prisons in California (USA). The subjects were randomly assigned to a 10-months program (GRIP, Guiding Rage Into Power) aiming at undoing the destructive behavioral patterns that lead to criminal actions by raising the individual’s 'mindfulness'. This study tests whether the participation to this program (treatment), based on strong relationships and mutual help, affects pro-social behavior of participants, in particular with reference to trust and inequality aversion. The research protocol entails the administration of two questionnaires including a set of behavioral situations ('games') - widely used in the relevant literature in the field - to 80 inmates, 42 treated (enrolled in the program) and 38 controls. The first questionnaire has been administered before treatment and randomization took place; the second questionnaire at the end of the program. The results of a Difference-in-Differences estimation procedure, show that trust significantly increases GRIP participants to compared to the control group. The result is robust to alternative estimation techniques and to the inclusion of a set of covariates to further control for idiosyncratic characteristics of the prisoners.

Keywords: behavioral economics, difference in differences, longitudinal study, pro-social preferences

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Procedia PDF Downloads 71