• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• Structural Engineering and Mechanics
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• 제86권3호
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• pp.337-348
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• 2023

The optimization of reinforced concrete (RC) cantilever retaining walls is a complex problem and requires the use of advanced techniques like metaheuristic algorithms. For this purpose, an optimization model must first be developed, which involves mathematical complications, multidisciplinary knowledge, and programming skills. This task has proven to be too arduous and has halted the mainstream acceptance of optimization. Therefore, it is necessary to unravel the complications of optimization into an easily applicable form. Currently, the most commonly used method for designing retaining walls is by following the proportioning limits provided by the ACI handbook. However, these limits, derived manually, are not verified by any optimization technique. There is a need to validate or modify these limits, using optimization algorithms to consider them as optimal limits. Therefore, this study aims to propose updated proportioning limits for the economical design of a RC cantilever retaining wall through a comprehensive parametric investigation using the genetic algorithm (GA). Multiple simulations are run to examine various design parameters, and trends are drawn to determine effective ranges. The optimal limits are derived for 5 geometric and 3 reinforcement variables and validated by comparison with their predecessor, ACI's preliminary proportioning limits. The results indicate close proximity between the optimized and code-provided ranges; however, the use of optimal limits can lead to additional cost optimization. Modifications to achieve further optimization are also discussed. Besides the geometric variables, other design parameters not covered by the ACI building code, like reinforcement ratios, bar diameters, and material strengths, and their effects on cost optimization, are also discussed. The findings of this investigation can be used by experienced engineers to refine their designs, without delving into the complexities of optimization.

• 대한의용생체공학회:의공학회지
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• 제44권3호
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• pp.190-203
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• 2023

The purpose of this study is to provide basic data to ensure the safety and essential performance of a Lower Extremity robotic assisted gait training system and to provide advanced technology and technical basis to the industry handling the system. Based on IEC 60601-1:2012/AMD2:2020 (Medical Electrical Equipment - General requirements for basic safety and essential performance of medical electrical equipment), IEC 62366-1:2015/AMD1:2020 (Medical devices - Part 1: Application of usability engineering to medical devices) and EN ISO 14971:2019 (Medical devices - Application of risk management to medical devices), the requirements for ensuring the safety and essential performance of the Lower Extremity robotic assisted gait training system were derived. Through the Delphi survey method and scenario analysis, which reflects the opinions and knowledge of experts in the fields of development, testing and review of technical documents, and quality assurance of medical devices, validity and reliability were conducted and obtained results with adequate content validity ratio (CVR; 0.7≤) and excellent reliability (Cronbach's α; 0.9≤). As a result, it was confirmed that the reliability and validity of the risk management process to ensure the safety and essential performance of the Lower Extremity robotic assisted gait training system are required a model can be established to provide measures to reduce risks according to the level of risk exposure caused by usage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2188-2208
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• 2023

Quality of Service (QoS) is a critical feature of Wireless Sensor Networks (WSNs) with routing algorithms. Data packets are moved between cluster heads with QoS using a number of energy-efficient routing techniques. However, sustaining high scalability while increasing the life of a WSN's networks scenario remains a challenging task. Thus, this research aims to develop an energy-balancing component that ensures equal energy consumption for all network sensors while offering flexible routing without congestion, even at peak hours. This research work proposes a Gravitational Blackhole Search Optimised splay tree routing framework. Based on the splay tree topology, the routing procedure is carried out by the suggested method using three distinct steps. Initially, the proposed GBSO decides the optimal route at initiation phases by choosing the root node with optimum energy in the splay tree. In the selection stage, the steps for energy update and trust update are completed by evaluating a novel reliance function utilising the Parent Reliance (PR) and Grand Parent Reliance (GPR). Finally, in the routing phase, using the fitness measure and the minimal distance, the GBSO algorithm determines the best route for data broadcast. The model results demonstrated the efficacy of the suggested technique with 99.52% packet delivery ratio, a minimum delay of 0.19 s, and a network lifetime of 1750 rounds with 200 nodes. Also, the comparative analysis ensured that the suggested algorithm surpasses the effectiveness of the existing algorithm in all aspects and guaranteed end-to-end delivery of packets.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• 이베로아메리카
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• 제21권1호
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• pp.159-206
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• 2019

The objective of the article is to describe the social structure of Uruguay at the beginning of the sixties, presenting the social differences that configured the level of urban centers or localities since the beginning of the 20th century. The willing is to identify what historical processes would have intervened to have institutionalized a highly heterogeneous distribution of welfare in the territory. The sources of information are four: (i) the population censuses of 1908 and 1963; (ii) the agricultural censuses of 1908, 1951, 1956 and 1961; (iii) the work of historical demography; and (iv) the indices published by the General Directorate of Statistics and Censuses in 1971 and 1989. Due to the lack of a dictionary of the localities, the authors matched these sources. The regional configuration processes analyzed here are: the departmentalization of the territory; the agriculturization; the industrialization and the balnearización. In each case, the regional and local impact of the political, economic and social transformation is analyzed. The article dedicate a wide space to describe the changes in the entity, the legal status, the territorial distribution and the wellbeing of the localities, marking especially those under 1500 inhabitants, which make up an important micro-urbanization of the country. The foundation of 70% of the localities occurred during the reformist period that locates that project as a type of "social democratic" State with its "Bismarkian" type traits. The institutionalization of these territories is based on their precarious and very poor character, the inequality in the most elementary welfare enjoyed by the established towns and cities increases. The processes of spatialization that reconfigured the territory during this time, contributed to the urbanization and population relocation, but in a framework in the political project of the "small model country" did not plan on the territory, the state have no special attention to correct the asymmetries in local welfare distribution structures. We finished with the hypothesis that beyond the social advances of the "Battlista" period, the territorial inequality of opportunities has grown between 1908 and 1963 because the lack of a public planification.

• 한국컴퓨터정보학회논문지
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• 제14권9호
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• pp.179-187
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• 2009

세계시장은 글로벌(Global)기업의 등장과 함께 국가 간 경계를 약화시켰으며, 제품의 설계, 마케팅 활동은 현지시장 위주, R&D 활동은 지식활동이 활발한 지역 단순 제품의 조립 제조는 저임금 지역으로 분산되고 있다. 이는 생산거점과 판매거점을 연결하는 네트워크(Network) 구성의 중요성이 대두되고 있다는 반증이다. 90년대 초부터는 다품종 소량생산, 고급화 및 소비자의 생산 프로세스 참여를 통한 수요창출 등 소비자 중심의 시장으로 다변화하고 있다. 이는 물류 체계에 있어 기술적 변화 요구에 적절히 대응하는 현상으로, 이를 증명하는 것이 e-비즈니스 등장과 이를 지원하는 부가가치 통신망(VAN: Value added Network) 전자문서교환(EDI : Electronic Data Interchange) 등이다. 본 연구에서는 국내외 육상, 해상 항공물류 시스템에 관한 문헌 연구를 통하여 각 시스템에 대한 실용분석을 시도하였으며, 한편으로 국내 항공사의 모델을 기초하여 항공사 물류시스템의 향후 개선 및 발전 방향을 제시하였다.

• Journal of Preventive Medicine and Public Health
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• 제55권2호
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• pp.205-212
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• 2022

Objectives: This study aimed to identify the behaviors associated with discrimination towards people living with HIV/AIDS (PLHA) in Indonesia and to determine the factors affecting discrimination. Methods: Secondary data from the 2017 Indonesia Demographic and Health Survey were analyzed using a cross-sectional design. Discrimination was assessed based on the questions (1) "Should children infected with HIV/AIDS be allowed to attend school with non-infected children?" and (2) "Would you buy fresh vegetables from a farmer or shopkeeper known to be infected with HIV/AIDS?" Multivariable logistic regression was used to determine the factors affecting discrimination, with adjusted odds ratio (aOR) and 95% confidence interval (CIs) used to show the strength, direction, and significance of the associations among factors. Results: In total, 68.9% of 21 838 individuals showed discrimination towards PLHA. The odds of discrimination were lower among women (aOR, 0.63; 95% CI, 0.55 to 0.71), rural dwellers (aOR, 0.81; 95% CI, 0.75 to 0.89), those who understood how HIV is transmitted from mother to child (aOR, 0.81; 95% CI, 0.73 to 0.89), and those who felt ashamed of their own family's HIV status (aOR, 0.56; 95% CI, 0.52 to 0.61). The odds were higher among individuals who knew how to reduce the risk of getting HIV/AIDS (aOR, 1.27; 95% CI, 1.15 to 1.39), how HIV/AIDS is transmitted (aOR, 3.49; 95% CI, 3.09 to 3.95), and were willing to care for an infected relative (aOR, 2.78; 95% CI, 2.47 to 3.13). A model consisting of those variables explained 69% of the variance in discrimination. Conclusions: Gender, residence, knowledge, and attitudes related to HIV/AIDS were explanatory factors for discrimination against PLHA. Improvements in HIV/AIDS education programs are needed to prevent discrimination.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.72-74
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• 2022

국가과학기술지식정보서비스(이하 NTIS)에서는 국가R&D과제정보를 제공하고 있다. 과제정보는 '과제명', '과제수행기관', '연구책임자명' 등의 메타정보와 '연구목표', '연구내용', '기대효과'와 같은 과제를 설명하는 텍스트들로 구성되어있다. 과제정보 100만건을 대상으로 검색한 결과목록에서 '연구목표' 나 '연구내용' 등을 모두 확인하여 원하는 과제정보를 찾기 위해서는 많은 시간이 필요하다는 문제가 있다. 이러한 문제점을 해소하기 위해, 본 논문에서는 국가R&D 과제정보 내에서 장문의 텍스트로 구성된 부분을 요약하는 과제정보 요약 시스템을 제안하고자 한다. 한국어의 언어학적 특징을 분석하여 전처리기를 구축하고 전처리된 텍스트 정보를 처리하기 위한 자연어 처리 기술 기반 과제정보 요약 모델을 개발하였다. 이를 통해 장문으로 구성된 과제정보를 압축 및 요약된 형태로 제공하여, 이용자들이 요약정보만으로도 전반적인 내용을 쉽고 빠르게 유추하는 데 도움이 될 것이다.

• 한국BIM학회 논문집
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• 제13권3호
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• pp.21-29
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• 2023

ChatGPT, a chatbot based on GPT large language models, has gained immense popularity among the general public as well as domain professionals. To assess its proficiency in specialized fields, ChatGPT was tested on mainstream exams like the bar exam and medical licensing tests. This study evaluated ChatGPT's ability to answer questions related to Building Information Modeling (BIM) by testing it on Korea's BIM expertise exam, focusing primarily on multiple-choice problems. Both GPT-3.5 and GPT-4 were tested by prompting them to provide the correct answers to three years' worth of exams, totaling 150 questions. The results showed that both versions passed the test with average scores of 68 and 85, respectively. GPT-4 performed particularly well in categories related to 'BIM software' and 'Smart Construction technology'. However, it did not fare well in 'BIM applications'. Both versions were more proficient with short-answer choices than with sentence-length answers. Additionally, GPT-4 struggled with questions related to BIM policies and regulations specific to the Korean industry. Such limitations might be addressed by using tools like LangChain, which allow for feeding domain-specific documents to customize ChatGPT's responses. These advancements are anticipated to enhance ChatGPT's utility as a virtual assistant for BIM education and modeling automation.