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Greenhouse Constructions Climatized With Electricity Generated By Photovoltaic System Integrated Into The Structurkishe
No full textBu çalışmada, yapıya entegre fotovoltaik sistem (YEFS) ile kendi enerjisini üreterek
iklimlendirilen sera yapılarının tasarım özellikleri incelenmiştir. Antalya yöresinde tasarımlanacak olan ve YEFS ile iklimlendirilecek olan bir cam sera için ısıtma veIn this study, the design feaTurkishes of the greenhouse strucTurkishes which are conditioned by
producing the own energy by the integrated photovoltaic system (YEFS) have been examined. Heating and cooling loads were evaluated for a glass greenhouse t
New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration
Get PDFAs the environments that human live are complex and uncontrolled, the object manipulation with humanoid robots is regarded as one of the most challenging tasks. Learning a manipulation skill from human Demonstration (LfD) is one of the popular methods in the artificial intelligence and robotics community. This paper introduces a deep learning based teleoperation system for humanoid robots that imitate the human operator's object manipulation behavior. One of the fundamental problems in LfD is to approximate the robot trajectories obtained by means of human demonstrations with high accuracy. The work introduces novel models based on Convolutional Neural Networks (CNNs), CNNs-Long Short-Term Memory (LSTM) models combining the CNN LSTM models, and their scaled variants for object manipulation with humanoid robots by using LfD. In the proposed LfD system, six models are employed to estimate the shoulder roll position of the humanoid robot. The data are first collected in terms of teleoperation of a real Robotis-Op3 humanoid robot and the models are trained. The trajectory estimation is then carried out by the trained CNNs and CNN-LSTM models on the humanoid robot in an autonomous way. All trajectories relating the joint positions are finally generated by the model outputs. The results relating to the six models are compared to each other and the real ones in terms of the training and validation loss, the parameter number, and the training and testing time. Extensive experimental results show that the proposed CNN models are well learned the joint positions and especially the hybrid CNN-LSTM models in the proposed teleoperation system exhibit a more accuracy and stable results
Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms
Get PDFTo reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented, and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms, M5RT and M5RGT, provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.184 and RMSE = 1.071, respectively. In order to recommend a practical solution, the tree structure algorithms are supplied to compute sediment transport in an open channel flow. © 2021 Gul et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
A Feminist Critique of Femvertising: The Example of Dove's "My Hair Is beyond the Rules" Campaign
Get PDFThis study uses Dove's (Turkish) TV advertisement "My Hair Is beyond the Rules" to develop a feminist critique of the new advertising method known as femvertising. The study begins by identifying four common points shared by post-feminist and femvertising discourse: (1) "love yourself" or "love your body," (2) "your decisions are important," (3) "trust yourself," and (4) "buy to feel good." The study then critiques these four points from a feminist perspective: The first point contradicts feminist body politics. The second point is a result of neoliberal individualism and contradicts the idea of feminist gathering. The third point misappropriates feminist political and ideological concepts by placing them into an apolitical cultural climate. And the last point embraces commodity feminism
An Adaptive Iterated Greedy algorithm for distributed mixed no-idle permutation flowshop scheduling problems
No full textDistributed flow shop scheduling is a very interesting research topic. This paper studies the distributed permutation flow shop scheduling problem with mixed no-idle constraints, which have important applications in practice. The optimization goal is to minimize total flowtime. A mixed-integer linear programming model is presented and an Adaptive Iterated Greedy (AIG) algorithm with the sample length changing according to the search process is designed. A restart strategy is also introduced to escape from local optima. Additionally, to further improve the performance of the algorithm, swap-based local search methods and acceleration algorithms for swap neighborhoods are proposed. Referenced Local Search (RLS), which shows better performance in solving scheduling problems, is also used in our algorithm. In the destruction stage, the job to be removed is selected according to the degree of influence on the total flowtime. In the initialization and construction phase, when a job is inserted, the jobs before and after the insertion position are removed and re-inserted into a better position to improve the algorithm search performance. A detailed design experiment is carried out to determine the best parameter configuration. Finally, large-scale experiments show that the proposed AIG algorithm is the best-performing one among all the algorithms in comparison
Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 1: Background, methodology, setup, and machine learning results
Get PDFDesigning high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings, previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However, different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings, as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase
Impact of information hiding on circular food supply chains in business-to-business context
Get PDFThis study has analyzed food supply chains from the circular economy viewpoint with a focus on knowledge hiding. In this paper, we examine the practice of hiding knowledge among stakeholders, in which dimension, what this knowledge is and whether there are differences among specific groups of stakeholders. This is applied to distributors, producers, consumers, retailers, suppliers and farmers working within the meat industry in Turkey. It shows how information hiding affects the traceability of food supply chains and circularity in the meat industry. Three different theories have been examined in this paper. Stakeholder theory helps to analyze traceability of food supply chains among stakeholders; the theory of industrial symbiosis aims to achieve an efficient circular food supply chain through 9R (refuse, rethink, reduce, reuse, repair, refurbish, remanufacture, repurpose, recycle, recover) strategies by adopting traceability; the information theory is a key enabler to coordinate traceability from farm to fork to support a circular food supply chain. By using the theoretical lens, this paper sets out proposals for policymakers and managers in food supply chains to ensure traceability and transparency to achieve circular economy. Although some prior studies address knowledge hiding, information hiding hasn't been examined with traceability and transparency dimensions in Circular Food Supply Chain (CFSC) in B2B business. This study attempts to fill this gap in the literature, improve theoretical understanding with our proposed framework and validating the impact of information hiding and reveal where knowledge is mostly hidden in terms of circular economy, stakeholders and 9Rs in the meat industry. A proposed framework for managers and policymakers based on a circular economy can bring social, economic and environmental benefits for the red meat industry in Turkey. Additionally, it offers a framework and recommendations for other countries and industries for possible adoption
FALCON OPTIMIZATION ALGORITHM FOR BAYESIAN NETWORK STRUCTURE LEARNING
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Abstract
In machine-learning, some of the helpful scientific models during the production of a structure of knowledge are Bayesian networks. They can draw the relationships of probabilistic dependency among many variables. The score and search method is a tool that is used as a strategy for learning the structure of a Bayesian network. The authors apply the falcon optimization algorithm (FOA) to the learning structure of a Bayesian network. This paper has employed reversing, deleting, moving, and inserting to obtain the FOA for approaching the optimal solution of a structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is associated with pigeon-inspired optimization, greedy search, and simulated annealing that apply the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques by utilizing several benchmark data sets. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values)
The effects of corporate governance practices on firm-level financial performance: Evidence from borsa istanbul xkury companies
Get PDForporate governance (CG) is a fundamental criteria for enhancing investors’ and stake-holders’ trust, relatively recently recognized in emerging markets. This study investigates the effects of CG practices on the firm-level financial performance of Borsa Istanbul XKURY-indexed companies during 2007–2019. Four specific aspects of CG are analysed: shareholders’ rights, public disclosure and transparency, stakeholders’ rights, and board of directors functioning, as defined by the Turkish Code of Corporate Governance, in line with international principles of CG issued by OECD. Alternative estimations of panel regression analysis indicate a positive association between stakeholder-oriented governance practices and firm-level financial performance expressed by accounting measures for both financial and non-financial companies. Shareholder protection policies have a negative influence on accounting-based performance, especially for non-financial industries, whereas the corporate practices related to board of directors and public disclosure vary between financial and non-financial entities. These findings contribute to international research on CG implications for emerging markets, providing evidence about the importance of stakeholders’ protection and the distinctive effects of CG dimensions for corporate financial performance