منو
بروندادهای پژوهشی
مرتب سازی بر اساس: سال انتشار
(نزولی)
Conference: 1 December 2014
Chemical Engineering and Technology (09307516) 37(12)pp. 2175-2184
A rate-based mathematical model was developed for the reactive absorption of H2S in NaOH, with NaOCl or H2O2 as the chemical oxidant solutions in a packed column. A modified mass transfer coefficient in the gas phase was obtained by genetic algorithm and implemented in the model to correct the assumption of instantaneous reactions. The effects of different operating variables including the inlet H2S concentration, inlet gas mass flux, initial NaOH, concentrations of the chemical oxidants in the scrubbing solutions, and liquid-to-gas ratio on the H2S removal efficiency were studied. A genetic algorithm was employed to optimize the operating variables in order to obtain maximum removal efficiency of H2S. The model results were in good agreement with the experimental data. A modified rate-based mathematical model was developed and evaluated in order to predict the removal efficiency of H2S in a packed-bed column with NaOH and chemical oxidant solutions as absorbents. Results of the validated model were adapted to a genetic algorithm to calculate optimal operating variables. Among the most effective operation parameters the initial pH of the alkaline solution was determined. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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pp. 104-118
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pp. 1-16
Soft power as one manifestation of power allows countries to gain influence through means such as po-litical values, cultural diplomacy, and foreign policy attitudes. Iraq has become a venue for influential players such as Iran and the US to exert their soft power after 2003. Iran employs shared religious ties with the Shiite population in Iraq, promotes its independent anti-imperialist foreign policy as a value, highlights the shared geography and historical memories between the two countries, and applies other similar elements to enhance its soft power. On the other hand, the United States employs cultural attrac-tions and promotes values such as freedom, democracy, and human rights. The United States presents itself as an idealistic democratic archetype and encourages Iraq to emulate its model in the post-Saddam era. The aim of this chapter is to provide a comparison of soft power status of Iran and the US in the post-Saddam Iraq. © 2024, IGI Global. All rights reserved.
One of the attacks in the RPL protocol is the Clone ID attack, that the attacker clones the node's ID in the network. In this research, a Clone ID detection system is designed for the Internet of Things (IoT), implemented in Contiki operating system, and evaluated using the Cooja emulator. Our evaluation shows that the proposed method has desirable performance in terms of energy consumption overhead, true positive rate, and detection speed. The overhead cost of the proposed method is low enough that it can be deployed in limited-resource nodes. The proposed method in each node has two phases, which are the steps of gathering information and attack detection. In the proposed scheme, each node detects this type of attack using control packets received from its neighbors and their information such as IP, rank, Path ETX, and RSSI, as well as the use of a routing table. The design of this system will contribute to the security of the IoT network. © 2021 IEEE.
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pp. 169-174
Task allocation, as an important issue in multi-agent systems (MAS), is defined as allocating the tasks to the agents such that maximum tasks are performed in minimum time. The vast range of application domains, such as scheduling, cooperation in crisis management, and project management, deal with the task allocation problem. Despite the plethora of algorithms that are proposed to solve this problem in different application domains, research on proposing a formalism for this problem is scarce. Such a formalism can be used as a way for better understanding and analyzing the behavior of real-world systems. In this paper, we propose a new formalism for specifying capability-based task allocation in MAS. The formalism can be used in different application domains to help domain experts better analyze and test their algorithms with more precision. To show the applicability of the formalism, we consider two algorithms as the case studies and formalize the inputs and outputs of these algorithms using the proposed formalism. The results indicate that our formalism is promising for specifying the capability-based task allocation in MAS at a proper level of abstraction. © 2021 IEEE.
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pp. 72-76
Question Answering is a hot topic in artificial intelligence and has many real-world applications. This field aims at generating an answer to the user's question by analyzing a massive volume of text documents. Answer Selection is a significant part of a question answering system and attempts to extract the most relevant answers to the user's question from the candidate answers pool. Recently, researchers have attempted to resolve the answer selection task by using deep neural networks. They first employed the recurrent neural networks and then gradually migrated to convolutional neural networks. Nevertheless, the use of language models, which is implemented by deep neural networks, has recently been considered. In this research, the DistilBERT language model was employed as the language model. The outputs of the Question Analysis part and Expected Answer Extraction component are also applied with [CLS] token output as the final feature vector. This operation leads to improving the method performance. Several experiments are performed to evaluate the effectiveness of the proposed method, and the results are reported based on the MAP and MRR metrics. The results show that the MAP values of the proposed method improved by 0.6%, and the MRR metric is improved by 0.2%. The results of our research show that using a heavy language model does not guarantee a more reliable method for answer selection problem. It also shows that the use of particular words, such as Question Word and Expected Answer word, can improve the performance of the method. © 2020 IEEE.
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pp. 99-105
Industry 4.0 provides a framework for applying new technologies in industrial environments to boost the efficiency and intelligence. A recently blossomed technology in Industry 4.0 is Internet of Things (IoT), which allows us to create a smart environment by connecting various equipment. One of the main applications of IoT in a smart factory is to design monitoring systems, which helps put the behavior of devices under permanent and comprehensive supervision. However, the rapid growth and change in the monitoring facilities creates a big challenge for people who either want to use that equipment in Industry 4.0, or want to update the systems to benefit from this technology. To address this problem, this paper presents new approach based on model-driven engineering paradigm, for simplifying the design and development of real-Time monitoring systems in an industrial environment. Our approach includes a domain-specific modeling language, a graphical editor, and model-To-code transformations that generate a hardware descriptive code, a mobile application, and a web application for a monitoring system. To evaluate the applicability of our approach, a scenario in the power industry has been designed, which offers user a VHDL code, a mobile application, and a web application for monitoring processes of the plant. © 2020 IEEE.
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pp. 302-307
A modeling language is a way to describe syntax, semantic, and constraints needed for creating models. Defining a Domain Specific Modeling Language (DSML) instead of suing a general-purpose one, increases the productivity of the developer as well as the quality of the resulted model. In this paper, we proposed a DSML for the Mitigation phase of Emergency Response Environments (EREs). We extended the TAO framework based on the TAO provided textual patterns. This paper also involves extending MAS-ML to support the modeling of EREs Mitigation phase. To evaluate this work, a case study is modeled with the proposed modeling language. Higher abstraction level, less effort, and faster development process are results of the proposed modeling language. © 2014 IEEE.
This paper presents a new method for coordinately tuning the parameters of UPFC controller and power system stabilizer (PSS) as well as determining the PSS location to enhance the stability of power system by using a new hybrid particle swarm optimization based co-evolutionary cultural algorithm, so called culture-PSO-co evolutionary (CPCE). Nonlinear simulations are implemented on the IEEE 39-bus power system. The results imply the effectiveness of the proposed method for damping out power system oscillations. © 2016 IEEE.
Daher, H. ,
Hoseindoost, S. ,
Zamani, B. ,
Fatemi, A. Publication Date: 0
pp. 35-41
In case of a disaster, planning for pedestrian evacuation from buildings is a major issue since it threatens human lives. To cope with this problem, evacuation plans are developed to ensure efficient evacuation in minimum time. These plans can be very sophisticated according to the complexity of the evacuation environment. This advocates the use of architectures such as Multi-Agent Systems (MAS) to develop the evacuation plans before happening of a real accident. Since developing an evacuation plan using MAS requires considerable effort, finding more efficient approaches is still an open problem. This paper introduces a new approach, based on the model-driven principles, to support developing evacuation plans. The approach includes utilizing a graphical editor for designing evacuation models, automatic generation of the evacuation plan code, as well as running the generated code on a MAS platform. We evaluated our approach using a case study. The results show that our approach provides elevated speed, less effort, high abstraction level, and more flexibility and productivity in developing emergency evacuation plans. © 2020 IEEE.
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pp. 188-193
Authorship Attribution (AA) is a task in which a disputed text is automatically assigned to an author chosen from a list of candidate authors. To this end, a model is trained on a dataset of textual documents with known authors, which can be considered as a multi-class single-label classification task. In this paper, we approach this task differently by extending information retrieval techniques to train an AA model. It is based on weighting the AARR technique, presented in our previous study, to relax the value of term frequency. The efficiency of the proposed solution has been evaluated by conducting several experiments on six datasets. The results show the superiority of the proposed solution by improving the accuracy of IMDB, Gutenberg books, Poetry, Blogs, PAN2011, and Twitter datasets by 33%, 31%, 31%, 19%, 6%, and 1%, respectively, where the average improvement is 19.94% over all datasets. The best accuracy over these datasets is 88%, 82%, 67%, 90%, 65%, and 81% in the same respect. In addition, compared to the baseline system, the computation time of the proposed solution has been improved significantly (21.44X) by employing a dictionary-based indexing technique. © 2021 IEEE.
Etahadtavakol, M. ,
Ng e.y.k., ,
Lucas, C. ,
Ataei, M. Publication Date: 0
pp. 255-274
Hemmat, A. ,
Vadaei, K. ,
Shirian, M. ,
Heydari, M.H. ,
Fatemi, A. Publication Date: 0
This paper introduces an innovative approach to Retrieval-Augmented Generation (RAG) for video question answering (VideoQA) through the development of an adaptive chunking methodology and the creation of a bilingual educational dataset. Our proposed adaptive chunking technique, powered by CLIP embeddings and SSIM scores, identifies meaningful transitions in video content by segmenting educational videos into semantically coherent chunks. This methodology optimizes the processing of slide-based lectures, ensuring efficient integration of visual and textual modalities for downstream RAG tasks. To support this work, we gathered a bilingual dataset comprising Persian and English mid- to long-duration academic videos, curated to reflect diverse topics, teaching styles, and multilingual content. Each video is enriched with synthetic question-answer pairs designed to challenge pure large language models (LLMs) and underscore the necessity of retrieval-augmented systems. The evaluation compares our CLIP-SSIM-based chunking approach against conventional video slicing methods, demonstrating significant improvements across RAGAS metrics, including Answer Relevance, Context Relevance, and Faithfulness. Furthermore, our findings reveal that the multimodal image-text retrieval scenario achieves the best overall performance, emphasizing the importance of integrating complementary modalities. This research establishes a robust framework for video RAG pipelines, expanding the capabilities of multimodal AI systems for educational content analysis and retrieval. © 2025 IEEE.
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pp. 155-160
A new control scheme, based on Artificial Neuro-Fuzzy Inference System (ANFIS) is used to design a robust Proportional Integral Derivative (PID) controller for Load Frequency Control (LFC). The controller algorithm is trained by the results of off-line studies obtained by using particle swarm optimization. The controller gains are optimized and updated in real-time according to load and parameters variations. Simulation results of this method on a multi-machine system in comparison with conventional fuzzy controller show the satisfactory results, especially where the parameters of the system change. © 2019 ICAI 2015 - WORLDCOMP 2015. All rights reserved.
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pp. 128-133
Due to the growning use of social networks and the use of viral marketing in these networks, finding influential people to maximize information diffusion is considered. This problem is Influence Maximization Problem on social networks. The main goal of this Problem is to select a set of influential nodes to maximize the influence spread in a social network. Researchers in this field have proposed different algorithms, but finding the influential people in the shortest possible time is still a challenge that has attracted the attention of researchers. Therefore, in this paper, the IMPT-C algorithm is presented with a focus on graph pre-processing in order to reduce the search space based on community structure. The approach of this algorithm is to take advantage of the topological properties of the graph to identify influential nodes. The experiment results indicate that the IMPT-C algorithm has a great influence spread with low run time compared the state-of-the-art algorithms consist least 2.36% improve than PHG in term the influence spread. © 2021 IEEE.
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2pp. 2883-2888
The area of agent-oriented methodologies is maturing rapidly and the time has come to begin drawing together the work of various research groups with the aim of developing the next generation of agent-oriented software engineering methodologies. An important step is to understand the differences between the various key methodologies, and to understand each methodology's strengths, weaknesses, and domains of applicability. In this paper we perform an investigation upon user views, on four well-known methodologies. We extend Tropos, as the most complete one up on users view point, by providing a proper supportive tool for it. © 2006 IEEE.
Lee s., S. ,
Mccarty g.w., G.W. ,
Lang, M.W. ,
Sadeghi, A. ,
Wallace, C. ,
Yeo i.-y., I. ,
Moglen g.e., G.E. Publication Date: 0
Understanding of wetland hydrology is important since wetland ecosystem services are dependent on hydrological interaction with surrounding areas. A perched layer is an impervious zone that limits horizontal water transport below land surface. This perched layer has been known to affect wetland hydrology. For example, the changing pattern of surface water (SW) is distinctive from shallow groundwater (GW) due to a perched layer. In addition, a wetland developed over a perched layer can have a high potential to hold SW than a wetland without a perched layer due to limited water loss by seepage. However, the impacts of a perched layer have not been well documented. This aim of this study is to investigate the impacts of a perched layer on wetland hydrology on the Coastal Plain of the Chesapeake Bay watershed using observed data. A well and piezometer were installed to monitor SW and GW at the two sites with and without a perched layer, respectively. By comparing observations between the two sites, we demonstrate that a wetland with a perched layer indicates inconsistency between SW and GW and less dependence on GW to maintain SW, compared to a wetland without a perched layer. © 2018 American Society of Agricultural and Biological Engineers. All rights reserved.
Mohammadi, A. ,
Hosseini, D. ,
Sarfjoo, Mohammad Reza ,
Mirsafaei, Razieh Publication Date: 0
Corrosion is a common phenomenon between materials and substances in their environment. Corrosion limits the use of metals for various purposes and increases costs in industries. Many advanced methods have been reported to prevent the corrosion of metal tools. This chapter discusses many topics related to corrosion mechanisms, inhibition routes, corrosion analysis, and mechanisms of waterborne polyurethane and its composites for corrosion protection. Waterborne polyurethane is an eco-friendly polymer that is ideal for a wide range of applications due to its properties, such as flexibility at low temperatures, moisture resistance, resistance to pH changes, quick drying, and easy cleaning. To create an effective coating, it is necessary to prepare highly stable dispersions with practical inhibitory effects, proper packing, high cross-linking density, suitable additive content, and strong adhesion to the substrate. In this chapter, the current literature and research on using waterborne polyurethane and its composites as an anti-corrosion coating are studied in detail to provide a comprehensive overview of how anticorrosion coatings work and what can improve their anti-corrosion properties. © 2023 Nova Science Publishers, Inc.
Lee s., S. ,
Sadeghi, A. ,
Yeo i.-y., I. ,
Mccarty g.w., G.W. ,
Hively w.d., W.D. ,
Lang, M.W. ,
Sharifi a., A. Publication Date: 0
Climate change is expected to exacerbate water quality degradation in the Chesapeake Bay Watershed (CBW). Winter cover crops (WCCs) have been widely implemented in this region due to their high effectiveness at reducing nitrate loads. However, little is known about climate change impacts on the effectiveness of WCCs for reducing nitrate loads. The objective of this study is to assess climate change impacts on WCC nitrate uptake efficiency on the Coastal Plain of the CBW using Soil and Water Assessment Tool (SWAT) model. We prepared climate change scenarios using General Circulation Models (GCMs) under three greenhouse emission scenarios (e.g., A1B, A2, and B1). Simulation results showed that WCC biomass increased by ∼ 58 % under climate change scenarios, due to climate conditions conducive to WCC growth. Prior to WCC implementation, annual nitrate loads increased by ∼ 43 % (5.3 kg N•ha-1) under climate change scenarios compared to the baseline scenario. When WCCs were planted, nitrate loads were substantially reduced and WCC nitrate reduction efficiency increased by ∼ 5 % under climate change scenarios relative to the baseline, due to increased WCC biomass. Therefore, the role of WCCs in mitigating nitrate loads should increase in the future given predicted climate change.
Baarslag, T. ,
Elfrink, T. ,
Nassiri-mofakham, F. ,
Koça, T. ,
Kaisers, M. ,
Aydogan, R. Publication Date: 0
pp. 390-397
This study presents Bargaining Chips: a framework for one-to-many concurrent composite negotiations, where multiple deals can be reached and combined. Our framework is designed to mirror the salient aspects of real-life procurement and trading scenarios, in which a buyer seeks to acquire a number of items from different sellers at the same time. To do so, the buyer needs to successfully perform multiple concurrent bilateral negotiations as well as coordinate the composite outcome resulting from each interdependent negotiation. This paper contributes to the state of the art by: (1) presenting a model and test-bed for addressing such challenges; (2) by proposing a new, asynchronous interaction protocol for coordinating concurrent negotiation threads; and (3) by providing classes of multi-deal coordinators that are able to navigate this new one-to-many multi-deal setting. We show that Bargaining Chips can be used to evaluate general asynchronous negotiation and coordination strategies in a setting that generalizes over a number of existing negotiation approaches. © 2021 Owner/Author.
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pp. 75-99
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41pp. 177-204
Hole Transport Materials (HTMs) play a pivotal role in a diverse array of cutting-edge optoelectronic devices prevalent in today's technological landscape. These materials are indispensable for the functionality and performance optimization of various technologies, including displays like Organic Light-Emitting Diodes (OLEDs) and photovoltaic devices like Perovskite Solar Cells (PSCs). The continuous advancement of OLEDs and PSCs over recent decades has spurred the innovation and development of a multitude of HTMs, each characterized by unique structural features. Presently, a notable trend is observed in the utilization of specific small organic molecules, such as carbazoles, as constituents of HTMs. Carbazole-based HTMs exhibit exceptional photovoltaic properties when compared to their counterparts and can be synthesized at a reduced cost, thus driving further exploration and refinement in this domain. Consequently, there is a concerted effort to gain comprehensive insights into the characteristics and capabilities of this category of HTMs, with a particular emphasis on Carbazole-Based Hole Transport Layers (HTLs). Therefore, Leveraging the intrinsic attributes of Carbazole-Based HTLs, researchers have focused on elucidating critical parameters such as Highest Occupied Molecular Orbital (HOMO)-Lowest Unoccupied Molecular Orbital (LUMO) energy levels, Glass Transition Temperatures (Tg), hole mobilities, among others. These data serve as invaluable assets for researchers engaged in interdisciplinary fields spanning chemistry, materials science, electrical engineering, and physics. This investigation stands as a succinct yet comprehensive resource, delving into the intricacies of Carbazole-Based HTLs within the contexts of OLEDs and PSCs. By offering valuable insights and understanding into the design and optimization of HTMs, this study serves as a guiding beacon for researchers, catalyzing further advancements in the field of optoelectronics. © 2024 Nova Science Publishers, Inc. All rights reserved.
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pp. 148-152
The Internet of Things (IoT) enables smart Things to communicate via the Internet. Things are growing in number, and their need for multiple resources in a complementary manner engenders serious problems in resource allocation. Combinatorial Auctions (CA) are the optimal market mechanism for allocating such indivisible bundles. Since the abundance of bundles in the IoT market makes it impossible to bid on all bundles, Things express their preferences on some (and not all) bundles to make the winner determination amenable. We address the winner determination problem by proposing an allocation mechanism based on social choice methods, which operates on the number of requested resources, the number of bundles, the offered price, and the preferred weight of each bundle. These methods include Borda, Copeland, Average without Misery, Least Misery, and Hare. Finally, we demonstrate the evaluation of these methods in terms of execution time and envy-freeness among the Things. © 2023 IEEE.
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pp. 429-463
Most of the weight of the proton exchange membrane (PEM) fuel cell stack is in the bipolar plates. The main function of bipolar plates is uniform distribution of gas reactants as well as distribution of cooling fluid (water or air) inside the fuel cell. Therefore, the plate design and the characteristics of the gas and cooling channels inside them are essential to the operation of the PEM. Although reactive gas channels and cooling channels perform separately, there are many similarities between them. For example, gas channels should be designed so that distribution of reactive gases on the electrode surfaces is uniform. Also, cooling channels should be designed so that temperature distribution inside the fuel cell is uniform. Further, pressure drop of reactive gases inside the gas channels and the fluid inside the cooling channels must be minimal. In this chapter, initially the characteristics, functions and making materials of bipolar plates along with the to make channels inside of them are investigated. Afterwards, gas channels, cooling channels and effects of the shape and size of channels on the PEM fuel cell performance are studied. Finally, different configurations of gas and cooling channel with emphasizing on the new configurations of these channel are researched simultaneously. © 2022 Elsevier Inc. All rights reserved.
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Engineering Materials (16121317) pp. 119-135
Solar cells based on semiconductor heterojunction demonstrate tunable interfaces and high efficiency, showing great potentials in future applications. In heterojunction solar cells, charge transport materials play critical roles in carrier conductivity, recombination kinetics, and charge collection efficiency, which in turn significantly influence the photovoltaic parameters as well as the stability of solar cells. Traditional inorganic and molecular conductors exhibit high promises in optoelectronic properties, however, they are somewhat facing challenges in high material cost, poor device stability, and sophisticated fabricating processes. Alternatively, conducting polymers have been recently recognized as promising charge transport materials due to their advantages of high conductivity, tunable work function, controllable transmittance, and high stability. Careful design and optimization of polymer chemical structures have promoted fast development in tuning their optoelectronic properties and enhancing photovoltaic performance. Therefore, in this chapter, we summarize the recent progress of strategies in designing new conducting polymer materials as a charge transport medium for solar cell application. The current challenges and prospects in the future development of polymer-based charge conductors are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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pp. 260-264
Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach to mitigate the limitations of large language models (LLMs) in answering domain-specific questions. Previous research has predominantly focused on improving the accuracy and quality of retrieved data chunks to enhance the overall performance of the generation pipeline. However, despite ongoing advancements, the critical issue of retrieving irrelevant information—which can impair a model’s ability to utilize its internal knowledge effectively—has received minimal attention. In this work, we investigate the impact of retrieving irrelevant information in open-domain question answering, highlighting its significant detrimental effect on the quality of LLM outputs. To address this challenge, we propose the Context Awareness Gate (CAG) architecture, a novel mechanism that dynamically adjusts the LLM’s input prompt based on whether the user query necessitates external context retrieval. Additionally, we introduce the Vector Candidates method, a core mathematical component of CAG that is statistical, LLM-independent, and highly scalable. We further examine the distributions of relationships between contexts and questions, presenting a statistical analysis of these distributions. This analysis can be leveraged to enhance the context retrieval process in retrieval-augmented generation (RAG) systems. © 2024 IEEE.
For a long time, culture has been an influencing parameter in negotiations. Growth of international trades and business competitions has increased the importance of negotiations among countries and different cultures. Developing new technologies, particularly the use of artificial intelligence in electronic trading areas, has provided us with the application of intelligent agents to resolve challenges in e- negotiations. In this study, a model is developed and implemented to arm intelligent agents with time-sensitivity cultural parameter in negotiations in electronic commerce context. The seller's proposals are offered based on the estimated value of the buyers' time-sensitivity in delivering the products. It starts from the highest price which satisfies the buyer's time sensitivity. The simulations are based on the Salacuse's Cultural dataset related to five countries, Finland, Mexico, Turkey, India, and the United States of America. The negotiation algorithms were implemented in Java platform and MySQL database for both cases of with and without cultural differences in time sensitivity. The evaluation shows that the cultural-based model starts the negotiation from an offer close to the buyer's desire. This yields less number of rounds and total negotiation time period. The simulation results also show that the buyer's budget as an economic factor can be effective in the negotiation outcomes in some cases. © 2014 IEEE.
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University of Isfahan
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