• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.1-6
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• 2023

Morphological analysis is a branch of natural language processing, is now a rapidly growing field. The fundamental tenet of morphological analysis is that it can establish the roots or stems of words and enable comparison to the original term. Arabic is a highly inflected and derivational language and it has a strong structure. Each root or stem can have a large number of affixes attached to it due to the non-concatenative nature of Arabic morphology, increasing the number of possible inflected words that can be created. Accurate verb recognition and extraction are necessary nearly all issues in well-known study topics include Web Search, Information Retrieval, Machine Translation, Question Answering and so forth. in this work we have designed and implemented an algorithm to detect and recognize Arbic Verbs from Arabic text.The suggested technique was created with "Python" and the "pyqt5" visual package, allowing for quick modification and easy addition of new patterns. We employed 17 alternative patterns to represent all verbs in terms of singular, plural, masculine, and feminine pronouns as well as past, present, and imperative verb tenses. All of the verbs that matched these patterns were used when a verb has a root, and the outcomes were reliable. The approach is able to recognize all verbs with the same structure without requiring any alterations to the code or design. The verbs that are not recognized by our method have no antecedents in the Arabic roots. According to our work, the strategy can rapidly and precisely identify verbs with roots, but it cannot be used to identify verbs that are not in the Arabic language. We advise employing a hybrid approach that combines many principles as a result.

• Computers and Concrete
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• v.32 no.2
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• pp.217-232
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• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• Nuclear Engineering and Technology
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• v.27 no.4
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• pp.571-581
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• 1995

The generation, verification and validation of Emergency Procedures for Nuclear Power Plant is a difficult and complex process. Atomic Energy Control Board(AECB) requires that emergency procedure and plan be produced before obtaining the Operating License, that is, detailed plans and procedures to handle emergency situations for both on-site actions and off-site actions be developed. In this report Emergency Operating Procedures Standard for Canadian Nuclear Utilities which makes reference to U. S. practices and the current direction of emergency procedures for CAN-DU reactors are reviewed and compared based on scope(events covered), methodology (event-oriented or symptom-oriented or hybrid) and format(method of presentation) preponderantly, and an attempt is made to integrate these procedures and as a result the recommended strategy for Wolsong unit 2, 3, & 4 is presented as event-specific procedures, generic procedures(when event is not diagnosed) and whose format is combination of logic diagram and text.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• Korean Journal of Materials Research
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• v.33 no.10
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• pp.377-384
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• 2023

Exploring earth-abundant, highly effective and stable electrocatalysts for electrochemical water splitting is urgent and essential to the development of hydrogen (H₂) energy technology. Iron-cobalt layered double hydroxide (FeCo-LDH) has been widely used as an electrocatalystfor OER due to its facile synthesis, tunable components, and low cost. However, LDH synthesized by the traditional hydrothermal method tends to easily agglomerate, resulting in an unstable structure that can change or dissolve in an alkaline solution. Therefore, studying the real active phase is highly significant in the design of electrochemical electrode materials. Here, metal-organic frameworks (MOFs) are used as template precursors to derive FeCo-LDH from different iron sources. Iron salts with different anions have a significant impact on the morphology and charge transfer properties of the resulting materials. FeCo-LDH synthesized from iron sulfate solution (FeCo-LDH-SO₄) exhibits a hybrid structure of nanosheets and nanowires, quite different from other electrocatalysts that were synthesized from iron chloride and iron nitrate solutions. The final FeCo-LDH-SO₄ had an overpotential of 247 mV with a low Tafel-slope of 60.6 mV dec^-1 at a current density of 10 mA cm^-2 and delivered a long-term stability of 40 h for the OER. This work provides an innovative and feasible strategy to construct efficient electrocatalysts.

• Asia pacific journal of information systems
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• v.22 no.2
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• pp.21-38
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• 2012

Until recently, successful implementation of ERP systems has been a popular topic among ERP researchers, who have attempted to identify its various contributing factors. None of these efforts, however, explicitly recognize the need to identify disparities that can exist between organizational information requirements and ERP systems. Since ERP systems are in fact "packages" -that is, software programs developed by independent software vendors for sale to organizations that use them-they are designed to meet the general needs of numerous organizations, rather than the unique needs of a particular organization, as is the case with custom-developed software. By adopting standard packages, organizations can substantially reduce many of the potential implementation risks commonly associated with custom-developed software. However, it is also true that the nature of the package itself could be a risk factor as the features and functions of the ERP systems may not completely comply with a particular organization's informational requirements. In this study, based on the organizational memory mismatch perspective that was derived from organizational memory theory and cognitive dissonance theory, we define the nature of disparities, which we call "mismatches," and propose that the mismatch between organizational information requirements and ERP systems is one of the primary determinants in the successful implementation of ERP systems. Furthermore, we suggest that customization efforts as a coping strategy for mismatches can play a significant role in increasing the possibilities of success. In order to examine the contention we propose in this study, we employed a survey-based field study of ERP project team members, resulting in a total of 77 responses. The results of this study show that, as anticipated from the organizational memory mismatch perspective, the mismatch between organizational information requirements and ERP systems makes a significantly negative impact on the implementation success of ERP systems. This finding confirms our hypothesis that the more mismatch there is, the more difficult successful ERP implementation is, and thus requires more attention to be drawn to mismatch as a major failure source in ERP implementation. This study also found that as a coping strategy on mismatch, the effects of customization are significant. In other words, utilizing the appropriate customization method could lead to the implementation success of ERP systems. This is somewhat interesting because it runs counter to the argument of some literature and ERP vendors that minimized customization (or even the lack thereof) is required for successful ERP implementation. In many ERP projects, there is a tendency among ERP developers to adopt default ERP functions without any customization, adhering to the slogan of "the introduction of best practices." However, this study asserts that we cannot expect successful implementation if we don't attempt to customize ERP systems when mismatches exist. For a more detailed analysis, we identified three types of mismatches-Non-ERP, Non-Procedure, and Hybrid. Among these, only Non-ERP mismatches (a situation in which ERP systems cannot support the existing information needs that are currently fulfilled) were found to have a direct influence on the implementation of ERP systems. Neither Non-Procedure nor Hybrid mismatches were found to have significant impact in the ERP context. These findings provide meaningful insights since they could serve as the basis for discussing how the ERP implementation process should be defined and what activities should be included in the implementation process. They show that ERP developers may not want to include organizational (or business processes) changes in the implementation process, suggesting that doing so could lead to failed implementation. And in fact, this suggestion eventually turned out to be true when we found that the application of process customization led to higher possibilities of failure. From these discussions, we are convinced that Non-ERP is the only type of mismatch we need to focus on during the implementation process, implying that organizational changes must be made before, rather than during, the implementation process. Finally, this study found that among the various customization approaches, bolt-on development methods in particular seemed to have significantly positive effects. Interestingly again, this finding is not in the same line of thought as that of the vendors in the ERP industry. The vendors' recommendations are to apply as many best practices as possible, thereby resulting in the minimization of customization and utilization of bolt-on development methods. They particularly advise against changing the source code and rather recommend employing, when necessary, the method of programming additional software code using the computer language of the vendor. As previously stated, however, our study found active customization, especially bolt-on development methods, to have positive effects on ERP, and found source code changes in particular to have the most significant effects. Moreover, our study found programming additional software to be ineffective, suggesting there is much difference between ERP developers and vendors in viewpoints and strategies toward ERP customization. In summary, mismatches are inherent in the ERP implementation context and play an important role in determining its success. Considering the significance of mismatches, this study proposes a new model for successful ERP implementation, developed from the organizational memory mismatch perspective, and provides many insights by empirically confirming the model's usefulness.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.83-102
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• 2008

Ubiquitous computing requires timely supply of contextual information in order to upgrade decision quality. In this sense, this study is aimed at proposing a multi-dynamic decision support system for highly ill-structured problems. Especially, it is very important for decision makers in the ubiquitous computing to coordinate conflicts among local goals and global goal harmoniously. The proposed Multi-Dynamic Decision Support System (MDDSS) is basically composed of both central structure and distributed structure, in which central structure supports multi objects decision making and distributed structure supports individual decision making. Its hybrid architecture consists of decision processor, multi-agent controller and intelligent knowledge management processor. Decision processor provides decision support using contexts which come from individual agents. Multi-agent controller coordinates tension among multi agents to resolve conflicts among them. Meanwhile, intelligent knowledge management processor manages knowledge to support decision making such as rules, knowledge, cases and so on. To prove the validity of the proposed MDDSS, we applied it to an u-fulfillment problem system in which many kinds of decision makers exist trying to satisfy their own objectives, and timely adjustment of action strategy is required. Therefore, the u-fulfillment problem is a highly ill-structured problem. We proved its effectiveness with the aid of multi-agent simulation comprising 60 customers and 10 vehicles under three experimental modes.

• Journal of the Korean Geographical Society
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• v.40 no.5 s.110
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• pp.473-490
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• 2005

The term hybridity has recently emerged as one of the most popularized leitmotivs in contemporary diasporic and transnational problematics on migrants' nomadic experiences. Especially, in postcolonial politics, hybridity is argued to provide a critical 'third space' on which to challenge discursive boundaries and redescribe power-embedded history However, this paper suggests that the hybrid subject position can be easily articulated in producing new cultural discourse and empowering hegemonic subjects in certain spates. Based on distinguishing the intentional, conscious hybridity from the organic, lived hybridity, this research Intends to investigate the Janus-faced, double-edged nature of the postcolonial politics of hybridity in the case of Los Angeles Koreatown. First, I discuss how a place of organic hybridity in Koreatown can lead to challenging invented and depoliticized ethnicity. At the second half of this paper, 1 focus on understanding the ways in which new Korean American professionals and elites employ the discourse of '1.5 generation' as an intentional hybridity for empowering their own political position at a local scale. I conclusively suggest that hybridity should be a deconstructive strategy to unlearn dominant socio-spatial boundaries rather than bring about the third space as a reterritorialized political position.

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.3
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• pp.149-155
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• 2020

Cattle feeding in South Korea has been heavily dependent on domestically produced rice straw and imported grain. Around 42% of domestically produced rice straw is utilized for forage, and the remainder is recycled to restore soil fertility. Approximately 35% of round baleages were made with rice straw. However, higher quality hay is desired over rice straw. Due to increasing stockpiles of rice, there has been an economic burden on the government to store the surplus; therefore production of annual forage crops in rice fields has been further promoted in recent years. Hay import from the USA currently constitutes more than 80% of total imported hays. The main imported hays are alfalfa (Medicago sativa), timothy (Phleum pretense), and tall fescue (Festica arundinacea). The estimated forage required for cattle feeding was approximately 5.4 million MT in 2016. Domestically produced forage sates only 43% of that value, while low quality rice straw and imported hay covered the rest of demand by 33% and 20%, respectively. As utilization of domestically produced forage is more desirable for forage-based cattle production, long-term strategies have been necessary to promote domestic production of high quality baleage. One such strategy has been utilizing the fertile soil and abundance of fallow rice fields of western region of S. Korea to produce forage crops. Italian ryegrass (Lolium multiflorum) is the most successfully produced winter annual in the region and is approximately 56% of the total winter annual forage production. Forage sorghums (Sorghum bicolor), sorghum × sudangrass hybrids, and hybrid corn (Zea mays) produce a substantial amount of warm-season forage during summer. Produced forage has been largely stored through baleage due to heavy dew and frequent rains and has been evaluated according to S. Korea's newly implemented baleage commodity evaluation system. The system weighs 50% of its total grading points on moisture content because of its importance in deliverable DM content and desirable baleage fermentation; this has proved to be an effective method. Although further improvement is required for the future of forage production in South Korea, the current government-led forage production in rice fields has been able to alleviate some of the country's shortage for quality hay.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.113-123
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• 2011

Recently, the global warming problem has arised around world, many nations has set up a various regulations for decreasing $CO_2$. In particular, $CO_2$ emissions reduction effect is very powerful in transport part, so there is a rising interest about development of green car, or electric vehicle in auto industry. For this reason, it is important to make a strategy for charging infra and forcast electric power demand, but it hasn't introduced about demand forecasting electric vehicle. Thus, this paper presents a demand forecasting for electric vehicles using choice based multigeneration diffusion model. In this paper, it estimates innovation coefficient, immitation coefficient in Bass model by using hybrid car market data and forecast electric vehicle market by year using potential demand market through SP(Stated Preference) experiment. Also, It facilitates more accurate demand forecasting electric vehicle market refelcting multigeneration diffusion model in accordance with attribute progress in development of electric vehicle. Through demand forecasting methodology in this paper, it can be utilized power supply and building a charging infra in the future.