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A Study on the Blockchain-Based Insurance Fraud Prediction Model Using Machine Learning (기계학습을 이용한 블록체인 기반의 보험사기 예측 모델 연구)

  • Lee, YongJoo
    • Journal of Convergence for Information Technology
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    • v.11 no.6
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    • pp.270-281
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    • 2021
  • With the development of information technology, the size of insurance fraud is increasing rapidly every year, and the method is being organized and advanced in conspiracy. Although various forms of prediction models are being studied to predict and detect this, insurance-related information is highly sensitive, which poses a high risk of sharing and access and has many legal or technical constraints. In this paper, we propose a machine learning insurance fraud prediction model based on blockchain, one of the most popular technologies with the recent advent of the Fourth Industrial Revolution. We utilize blockchain technology to realize a safe and trusted insurance information sharing system, apply the theory of social relationship analysis for more efficient and accurate fraud prediction, and propose machine learning fraud prediction patterns in four stages. Claims with high probability of fraud have the effect of being detected at a higher prediction rate at an earlier stage, and claims with low probability are applied differentially for post-reference management. The core mechanism of the proposed model has been verified by constructing an Ethereum local network, requiring more sophisticated performance evaluations in the future.

QLGR: A Q-learning-based Geographic FANET Routing Algorithm Based on Multi-agent Reinforcement Learning

  • Qiu, Xiulin;Xie, Yongsheng;Wang, Yinyin;Ye, Lei;Yang, Yuwang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.11
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    • pp.4244-4274
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    • 2021
  • The utilization of UAVs in various fields has led to the development of flying ad hoc network (FANET) technology. In a network environment with highly dynamic topology and frequent link changes, the traditional routing technology of FANET cannot satisfy the new communication demands. Traditional routing algorithm, based on geographic location, can "fall" into a routing hole. In view of this problem, we propose a geolocation routing protocol based on multi-agent reinforcement learning, which decreases the packet loss rate and routing cost of the routing protocol. The protocol views each node as an intelligent agent and evaluates the value of its neighbor nodes through the local information. In the value function, nodes consider information such as link quality, residual energy and queue length, which reduces the possibility of a routing hole. The protocol uses global rewards to enable individual nodes to collaborate in transmitting data. The performance of the protocol is experimentally analyzed for UAVs under extreme conditions such as topology changes and energy constraints. Simulation results show that our proposed QLGR-S protocol has advantages in performance parameters such as throughput, end-to-end delay, and energy consumption compared with the traditional GPSR protocol. QLGR-S provides more reliable connectivity for UAV networking technology, safeguards the communication requirements between UAVs, and further promotes the development of UAV technology.

Analysis of 3D Building Construction Applications in Augmented Reality

  • Khan, Humera Mehfooz;Waseemullah, Waseemullah;Bhutto, Muhammad Aslam;Khan, Shariq Mahmood;Baig, Mirza Adnan
    • International Journal of Computer Science & Network Security
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    • v.22 no.10
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    • pp.340-346
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    • 2022
  • Construction industry is considered as one of the oldest industries in the world since human came into being and the need of their own space is realized. All this led to make the world a space of many beautiful constructive ventures. As per the requirements of today's world, every industry is recognizing the need for use and adoption of modern as well as innovative technologies due to their benefits and timely production. Now construction industry has also started adopting the use of modern and innovative technologies during their projects but still the rate of adoption is so slow. From design to completion, construction projects take a lot to manage for which technology based solutions have continuously been proposed. These include Computer Aided Design (CAD), building information modeling (BIM) and cloud computing have been proved to be much successful until now. The construction projects are high budgeted, and direly require timely and successful completion with quality, resource and other constraints. So, the researchers observe the need of more clear and technology based communication between the construction projects and its constructors and other stakeholders is required before and during the construction to take timely precautions for expected issues. This study has analyzed the use of Augmented Reality (AR) technology adopting GammaAR, and ARki applications in construction industry. It has been found that both applications are light-weighted, upgradable, provide offline availability and collaborative environment as well as fulfil most of the requirements of the construction industry except the cost. These applications also support different screen size for better visualization and deep understanding. Both applications are analyzed, based on construction's application requirements, usability of AR and ratings of applications user collected from application's platform. The purpose of this research is to provide a detail insight of construction applications which are using AR to facilitate both the future developers and consumers.

Reinforced concrete structures with damped seismic buckling-restrained bracing optimization using multi-objective evolutionary niching ChOA

  • Shouhua Liu;Jianfeng Li;Hamidreza Aghajanirefah;Mohammad Khishe;Abbas Khishe;Arsalan Mahmoodzadeh;Banar Fareed Ibrahim
    • Steel and Composite Structures
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    • v.47 no.2
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    • pp.147-165
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    • 2023
  • The paper contrasts conventional seismic design with a design that incorporates buckling-restrained bracing in three-dimensional reinforced concrete buildings (BRBs). The suboptimal structures may be found using the multi-objective chimp optimization algorithm (MEN-ChOA). Given the constraints and dimensions, ChOA suffers from a slow convergence rate and tends to become stuck in local minima. Therefore, the ChOA is improved by niching and evolutionary operators to overcome the aforementioned problems. In addition, a new technique is presented to compute seismic and dead loads that include all of a structure's parts in an algorithm for three-dimensional frame design rather than only using structural elements. The performance of the constructed multi-objective model is evaluated using 12 standard multi-objective benchmarks proposed in IEEE congress on evolutionary computation. Second, MEN-ChOA is employed in constructing several reinforced concrete structures by the Mexico City building code. The variety of Pareto optimum fronts of these criteria enables a thorough performance examination of the MEN-ChOA. The results also reveal that BRB frames with comparable structural performance to conventional moment-resistant reinforced concrete framed buildings are more cost-effective when reinforced concrete building height rises. Structural performance and building cost may improve by using a nature-inspired strategy based on MEN-ChOA in structural design work.

An intelligent optimization method for the HCSB blanket based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network

  • Wen Zhou;Guomin Sun;Shuichiro Miwa;Zihui Yang;Zhuang Li;Di Zhang;Jianye Wang
    • Nuclear Engineering and Technology
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    • v.55 no.9
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    • pp.3150-3163
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    • 2023
  • To improve the performance of blanket: maximizing the tritium breeding rate (TBR) for tritium self-sufficiency, and minimizing the Dose of backplate for radiation protection, most previous studies are based on manual corrections to adjust the blanket structure to achieve optimization design, but it is difficult to find an optimal structure and tends to be trapped by local optimizations as it involves multiphysics field design, which is also inefficient and time-consuming process. The artificial intelligence (AI) maybe is a potential method for the optimization design of the blanket. So, this paper aims to develop an intelligent optimization method based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network to solve these problems mentioned above. This method has been applied on optimizing the radial arrangement of a conceptual design of CFETR HCSB blanket. Finally, a series of optimal radial arrangements are obtained under the constraints that the temperature of each component of the blanket does not exceed the limit and the radial length remains unchanged, the efficiency of the blanket optimization design is significantly improved. This study will provide a clue and inspiration for the application of artificial intelligence technology in the optimization design of blanket.

Complex organic molecules detected in twelve high mass star forming regions with ALMA

  • Baek, Giseon;Lee, Jeong-Eun;Hirota, Tomoya;Kim, Kee-Tae
    • The Bulletin of The Korean Astronomical Society
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    • v.46 no.2
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    • pp.37.3-38
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    • 2021
  • One of the key questions on star formation is how the organic molecules are synthesized and delivered to the planets and comets since they are the building blocks of prebiotic molecules such as amino acid, which is thought to contribute to bringing life on Earth. Recent astrochemical models and experiments have explained that complex organic molecules (COMs; molecules composed of six or more atoms) are produced on the dust grain mantles in cold and dense gas in prestellar cores. However, the chemical networks and the roles of physical conditions on chemistry are not still understood well. To address this question, hot (> 100 K) cores in high mass young stellar objects (M > 8 Msun) are great laboratories due to their strong emissions and larger samples than those of low-mass counterparts. In addition, CH3OH masers, which have been mostly found in high mass star forming regions, can provide constraints due to their very unique emerging mechanisms. We investigate twelve high mass star forming regions in ALMA band 6 observation. They are associated with 44/95 GHz Class I and 6.7 GHz Class II CH3OH masers, implying that the active accretion processes are ongoing. For these previously unresolved regions, 66 continuum peaks are detected. Among them, we found 28 cores emitting COMs and specified 10 cores associated with 6.7 GHz Class II CH3OH masers. The chemical diversity of COMs is found in cores in terms of richness and complexity; we identified up to 19 COMs including oxygen- and nitrogen-bearing molecules and their isotopologues in a core. Oxygen-bearing molecules appear to be abundant and more complex than nitrogen-bearing species. On the other hand, the COMs detection rate steeply grows with the gas column density, which can be attributed to the effective COMs formation in dense cores.

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Open BIS Platform and Business Model Development for Providing Bus Information in the Area (지역의 버스정보 제공을 위한 Open BIS 플랫폼 및 비즈니스 모델 개발)

  • Won pyoung Kang;Yung sung Cho;Seung neo Son;Hyo kyung Eo;Kyung suk Kim
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.23 no.1
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    • pp.97-111
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    • 2024
  • Developing countries and small local governments face financial constraints, limiting the adoption of their own bus information systems. However, despite poor social infrastructure and low income levels, developing countries have a high smartphone penetration rate, and the distribution and usage of online content and social media are widespread. Smartphones, equipped with GPS sensors, cameras, and other location-based information collection capabilities, can replace expensive on-site terminals. This study aims to replace expensive on-site terminals with smartphones, develop a center system based on cloud servers, and establish an extensible Open BIS (Bus Information System) service and platform that can be applied anywhere. The goal is to formulate a business model in the process.

A Study of 5G Systems to Improve Receiver Performance in the mmWave Band (밀리미터파 대역의 수신 성능을 개선하기 위한 5G 시스템에 대한 연구)

  • Myeong-saeng Kim;Dong-ok Kim
    • Journal of Advanced Navigation Technology
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    • v.28 no.3
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    • pp.362-368
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    • 2024
  • In this paper, we investigated the performance of directional and omnidirectional precoding schemes when transmitting to improve downlink performance in massive MIMO. Omnidirectional precoding was used to broadcast a common signal, such as a synchronization or control signal, to all users. The main purpose of omnidirectional precoding is to design the precoding matrix so that the signal transmitted in the downlink is the same in all directions and emitted with maximum energy. We propose a flexible omnidirectional precoding method for full-dimensional massive MIMO that can set the spatial coverage range to less than 120 degrees. The constraints of omnidirectionality of all antennas, equal transmit power, and maximum transmit rate are used to design the encoding matrix of the proposed method. The performance was evaluated in terms of spatial coverage by considering changing the spatial coverage of the antenna array by changing the distance between neighboring antennas in the antenna array.

Deep-Learning-Based Mine Detection Using Simulated Data (시뮬레이션 데이터 기반으로 학습된 딥러닝 모델을 활용한 지뢰식별연구)

  • Buhwan Jeon;Chunju Lee
    • Journal of The Korean Institute of Defense Technology
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    • v.5 no.4
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    • pp.16-21
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    • 2023
  • Although the global number of landmines is on a declining trend, the damages caused by previously buried landmines persist. In light of this, the present study contemplates solutions to issues and constraints that may arise due to the improvement of mine detection equipment and the reduction in the number of future soldiers. Current mine detectors lack data storage capabilities, posing limitations on data collection for research purposes. Additionally, practical data collection in real-world environments demands substantial time and manpower. Therefore, in this study, gprMax simulation was utilized to generate data. The lightweight CNN-based model, MobileNet, was trained and validated with real data, achieving a high identification rate of 97.35%. Consequently, the potential integration of technologies such as deep learning and simulation into geographical detection equipment is highlighted, offering a pathway to address potential future challenges. The study aims to somewhat alleviate these issues and anticipates contributing to the development of our military capabilities in becoming a future scientific and technological force.

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Industrial Policy as a Development Strategy: Cuba' s Experience and Policy Implications (개발전략으로서 산업정책: 쿠바의 경험과 정책적 시사점)

  • Cin, Beom Cheol
    • International Area Studies Review
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    • v.22 no.3
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    • pp.3-27
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    • 2018
  • This paper analyzes Cuba's market-oriented reforms to alleviate essential problems with socialist countries such as soft budget constraints and incentive problems. It also discuss about effectiveness of industrial policy as a development strategy. The soft budget constraints and incentive problems resulted in the collapse of Soviet bloc and COMECON in early 1990s. After the collapse, Cuban economy suffered a steep dive, and national income tumbling down rapidly. Cuban faced serious shortages of food, gasoline, and other basic necessities of life. To halt and partially reverse economic downturn and dire austerity in the 1990's, the Cuban government made some partial reforms to the inherited Soviet system of cental planningand faced severe shortage in food, energy, and daily necessities. In response to the economic crisis. Cuba introduced economic reforms and implemented industrial policy as a development strategy as long as Cuba maintained a strong socialist country. Cuban government established the economic free zone law and attempted to induce foreign direct investment by implementing export-led industrial policy. Fiedel Castro approved the Law No. 165 "Free Zones and Industrial Parks", in 1996. However, Cuba's ESZ strategy seems to have failed because of the U.S. sanctions, but also because of Cuba's own policies, which do not allow foreign investors to hire workers directly and impose a high implicit tax on wages. By limiting advanced techniques of personnel and organization management, indirect employment can result in lowering work efforts and productivity of workers, and aggravating production efficiency in the ESZs. Another reason to fail comes from the double wage structure due to the double monetary-exchange rate system. Most of the high non-wage costs result from the double exchange rate system. Due to Cuba's imbalanced industry and production structures, concentrated labor force, and urbanization and centralization of agriculture production, the industrial transformation development model suggested by Lewis has not been successful unlike other Asian agriculture-led development model. Cuba has to overcome many difficulties in implementing industrial policy as a development strategy.