فهرست مطالب

Journal of Computer and Robotics
Volume:16 Issue: 1, Winter and Spring 2023

  • تاریخ انتشار: 1402/01/07
  • تعداد عناوین: 6
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  • Hosniyeh Safiarian, MohammadJafar Tarokh *, Mohammadali Afshar Kazemi Pages 1-9

    When, we consider the ubiquity of Facebook, twitter, LinkedIn, it is easy to understand how social media is woven into the fabric of our day-to-day activities. It is a suitable tool to find information about news , events , and different Issues. After corona virus outbreak, it is inspired users to understand pandemic news, mortality statistics and vaccination news. According to evidence, the diffusion of pandemic news on social medium has increased from 2020 and user face a ton of COVID19 messages. The purpose of this paper is to determine the check-worthiness of news about COVID-19 to identify and priorities news that need fact-checking. We proposed a method that is called CVMD. We extracted the feature of content. We use the deep learning approach for prediction it means that we model this problem with a binary classification problem. Our proposed method is evaluated by different measures on twitter dataset and the results show that CVMD method has a high accuracy in prediction rather than other methods.

    Keywords: Check-Worthiness, Covid19, deep learning, Diffusion, social media
  • MohammadReza Nasiri * Pages 11-23

     Electrical output power fluctuations of wind farms inject poor quality of power into the grid. This problem is more remarkable for the wind farms utilizing fixed speed wind turbine generators (WTGs). In this paper the output power of a 10MW wind farm with 24 fixed speed WTGs, is investigated. The most appropriate location for power smoothing based on the short-term power recording and effective power oscillation frequency is determined. A transformerless cascaded H-bridge STATCOM (CHB STATCOM) combined with a mechanically switched capacitor (MSC) is proposed to compensate variable reactive power of the wind farm, as well as to smooth the short-term active power fluctuations. The active power flattening is accomplished by proper sizing of the CHB dc link capacitors according to necessary energy exchange. By comparing several distributed and centralized schemes, a 2MVar CHB STATCOM, which is economically justified, is proposed. The STATCOM performance for improving power quality of the wind farm is investigated by applying several power profiles acquired from the wind farm using simulink MATLAB environment.

    Keywords: key words—CHB STATCOM, Wind farm, fixed speed WTG, Power Quality, short-term power fluctuation smoothing
  • Zeinab Zarrat Dakheli Parast, Hassan Haleh *, Soroush Avakh Darestani, Hamzeh Amin Tahmasbi Pages 25-40
    The achievement of chain greening objectives, besides costs minimization, can be realized when both reverse and forward flows are taken into account in the design of the supply chain network. It is possible to decrease the chain costs and have a greener chain by means of different strategies like vehicular routing, hub location, inventory management, and simultaneous pickup and delivery. The development of green reverse supply chains and the practice of the above-mentioned strategies are becoming increasing important with the appearance of perishable product chains. Considering the mentioned points, the current study attempts to design a green reverse supply chain network for the purpose of distributing dairy items such as yogurt drink where the strategy of simultaneous pickup and delivery under uncertainty is taken into consideration. This model focuses on the simultaneous costs reduction and also decrease of lost demands and presents a fuzzy solution for solving the bi-objective model.
    Keywords: green reverse network, location-inventory-routing problem, Mathematical programming model, fuzzy solution approach, Dairy Industry
  • Hamid Yasinian, Mansour Esmaeilpour * Pages 41-56
    Successful future urban planning is highly dependent on optimal connectivity between important areas of cities. Discovering essential latent links will optimize the urban structure. Moving towards a better structure requires some information. There are a lot of sources of information for urban structure inferring, including the current structure, the time-varying traffic dynamics, and the construction costs, which are the basics of the optimization problem formulation. This paper presents a new formulation for the problem. The model problem to be solved tries to utilize all data sources needed for inferring. There are some methods for solving the formulated problem. The methods need some development to apply to the model. Methods utilizing learning automata (LA) are very favorable in this field due to the interaction with the environment. This paper presents two LA-based approaches for the model: Distributed Learning Automata (DLA) and Cellular Learning Automata (CLA). The algorithms result in an optimal connectivity matrix considering urban structure, traffic dynamics, and costs, where the matrix must include the current urban structure and some new reasonable necessary links. Moreover, comparisons are possible because the model has a fitness value for evaluating the provided connectivity matrix. The CLA-based proposed method performed better than the others in most experiments.
    Keywords: Urban Structure, traffic dynamics, optimal connectivity structure, Distributed Learning Automata, Cellular Learning Automata
  • Zohre Sadeghian, Ebrahim Akbari *, Hossein Nematzadeh, Homayun Motameni Pages 57-74

    Feature selection is the process of identifying relevant features and removing irrelevant and repetitive features with the aim of observing a subset of features that describe the problem well and with minimal loss of efficiency. One of the feature selection approaches is using optimization algorithms. This work provides a summary of some meta-heuristic feature selection methods proposed from 2018 to 2021 that were designed and implemented on a wide range of different data. The results of the study showed that some meta-heuristic algorithms alone cannot perfectly solve the feature selection problem on all types of datasets with an acceptable speed. In other words, depending on dataset, a special meta-heuristic algorithm should be used.

    Keywords: Data dimension reduction, Classification, Feature Selection, Optimization algorithm, Meta-Heuristic Algorithms
  • Mona Naghdehforoushha, Mehdi Dehghan Takht Fooladi, MohammadHossein Rezvani *, MohammadMehdi Gilanian Sadeghi Pages 75-87

    Today, cloud markets, especially Amazon, have attracted a lot of attention from users due to the provision of Spot Virtual Machines (SVMs). It has several advantages for both sides of the market. On the one hand, Amazon can generate revenue from its underutilized virtual machines. On the other hand, the customer can get the SVM as needed at a dynamic price through an auction method. Providing optimal bidding strategies in such a market is a crucial challenge. The bidding price is affected by uncertain parameters such as the price of SVMs, the number of available SVMs, the number of current customers, and their bidding values. In this paper, we use Information Gap Decision Theory (IGDT) to determine the best bidding strategy. Our proposed method includes both risk-averse and risk-neutral strategies. The evaluation results based on historical Amazon EC2 prices confirm the effectiveness of the proposed method in the presence of uncertain prices. It has high performance compared to the baseline methods in terms of robustness cost, uncertainty budget, and execution time.

    Keywords: Cloud spot market, bidding strategy, Uncertainty, Information Gap Decision Theory (IGDT)