• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• KNOM Review
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• 제22권3호
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• pp.1-12
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• 2019

최근 빅데이터와 AI를 이용한 연구들이 ICT 분야에서 주요 이슈로 부상하고 있다. 하지만 연구를 위한 빅데이터의 크기가 기하급수적으로 증가하면서 기존 네트워크 방식의 데이터 전송에 대해 사용자들은 빅데이터를 송수신하는데 걸리는 시간은 하드디스크를 복사하여 보내는 시간보다 느리다는 문제를 제기한다. 이에 따라 연구자들은데이터를 고속으로 전송하고, 다양한 네트워크의 구조를 수용할 수 있는 동적이고 유연한 네트워크 기술을 요구한다. SDN/NFV 기술은 네트워크를 프로그래밍하여 사용자들의 요구에 적절한 네트워크를 제공할 수 있는 기술로써, 네트워크의 유연성 및 보안성 문제를 해결할 수 있다. 또한 AI를 수행하는데 있어 문제가 되는 중앙집중적 방식의데이터 처리는 실시간성을 보장할 수 없고, 트래픽이 증가하는 경우 네트워크 지연이 발생한다. 이를 해결하기 위해 중앙집중적 방식을 탈피한 Edge-Computing 기술을 이용하여 해결할 수 있다. 본 논문에서는 SDN, NFV, Edge-Computing 기술에 대한 개념 및 연구 동향에 대해 알아보고, 세 가지 기술을 접목시켜 사용되는 데이터 중심 네트워크 기술 동향에 대해 분석한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1216-1233
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• 2023

Conventional occupational therapy (OT) is conducted under the observation of an occupational therapist, and there are limitations in measuring and analyzing details such as degree of hand tremor and movement tendency, so this important information may be lost. It is therefore difficult to identify quantitative performance indicators, and the presence of observers during performance sometimes makes the subjects feel that they have to achieve good results. In this study, by using the Unity3D and artificial intelligence (AI) speaker, we propose a system that allows the subjects to steadily use it by themselves and helps the occupational therapist objectively evaluate through quantitative data. This system is based on the OT of the sensory integration approach. And the purpose of this system is to improve children's activities of daily living by providing various feedback to induce sensory integration, which allows them to develop the ability to effectively use their bodies. A dynamic OT cognitive assessment tool for children used in clinical practice was implemented in Unity3D to create an OT environment of virtual space. The Leap Motion Controller allows users to track and record hand motion data in real time. Occupational therapists can control the user's performance environment remotely by connecting Unity3D and AI speaker. The experiment with the conventional OT tool and the system we proposed was conducted. As a result, it was found that when the system was performed without an observer, users can perform spontaneously and several times feeling ease and active mind.

• Imaging Science in Dentistry
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• 제53권1호
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• pp.77-82
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• 2023

Purpose: This study evaluated anatomical variations in the root canals of the lower premolars and molars in a Brazilian sub-population using cone-beam computed tomography (CBCT). Materials and Methods: In total, 121 CBCT images of patients were selected from a database. All images contained lower first and second premolars and molars on both sides of the arch, fully developed roots, and no treatment, resorption, or calcifications. In each image, the root canals of the lower premolars and molars were evaluated according to the Vertucci classification in On-Demand 3D software in the multiplanar reconstruction with dynamic navigation. Twenty-five percent of the images were re-assessed to analyze intraobserver confidence with the kappa test. Data were statistically evaluated with linear regression to evaluate the correlations of anatomic variations with age and sex, and the Wilcoxon test to analyze the laterality of variations, with a significance level of 5%. Results: The intraobserver agreement (0.94) was excellent. In general, the root canals of lower premolars and molars showed a higher prevalence of type I than other Vertucci classification types, followed by type V in premolars and type II in molars. When the molar roots were evaluated separately, type II was more frequent in mesial roots and type I in distal roots. Although age showed no correlations with the results, sex and laterality showed correlations with tooth 45 and the lower second premolars, respectively. Conclusion: The lower premolars and molars of a Brazilian sub-population showed a wide range of root canal anatomic variations.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.219-230
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• 2023

Wireless Body Area Networks (WBANs) have made it easier for healthcare workers and patients to monitor patients' status continuously in real time. WBANs have complex and diverse network structures; thus, management and control can be challenging. Therefore, considering emerging Software-defined networks (SDN) with WBANs is a promising technology since SDN implements a new network management and design approach. The SDN concept is used in this study to create more adaptable and dynamic network architectures for WBANs. The study focuses on comparing the performance of two SDN controllers, POX and Ryu, using Mininet, an open-source simulation tool, to construct network topologies. The performance of the controllers is evaluated based on bandwidth, throughput, and round-trip time metrics for networks using an OpenFlow switch with sixteen nodes and a controller for each topology. The study finds that the choice of network controller can significantly impact network performance and suggests that monitoring network performance indicators is crucial for optimizing network performance. The project provides valuable insights into the performance of SDN-based WBANs using POX and Ryu controllers and highlights the importance of selecting the appropriate network controller for a given network architecture.

• 한국전산구조공학회논문집
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• 제37권1호
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• pp.17-24
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• 2024

본 논문은 조선시대의 대표적인 중층 목구조인 공주 마곡사 대웅보전에 대하여 수직하중에 대한 구조성능을 평가하였다. 구조해석 소프트웨어인 midas Gen으로 실물과 근접하게 해석모델을 3차원으로 구축하였다. 정적해석으로 수직하중에 대한 주요 수직 및 수평 부재의 안전성과 사용성을 평가하였다. 모든 부재가 안전성과 사용성 기준을 만족하였으나, 하층 대량은 전이보 역할로 구조적 취약점이 나타나 개선의 필요가 있다. 동적거동특성 평가를 위한 고유치해석시 주요 접합부의 상대회전강성은 5%로 가정하였다. 고유주기는 1.105초로 비슷한 규모의 한옥 범주에 속하고 있으며, 1차 모드는 건물 전후방향의 병진운동으로 나타났다.

• 한국의료질향상학회지
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• 제30권1호
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• pp.55-75
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• 2024

Purpose: This study aimed to elucidate the intention to use non-face-to-face treatment, which was temporarily allowed during the coronavirus disease 2019 pandemic. Based on the social cognitive theory, individual behavioral changes occur through the dynamic interaction of individual, environmental, and behavioral factors. Thus, we investigated the impact of personal, environmental, and behavioral factors on the acceptance of non-face-to-face treatment. Methods: A Web survey was conducted using Korea Research Panel between December 26 and 29, 2022, to examine the conceptual framework. The survey targeted adults aged 19 and older, regardless of whether they had used non-face-to-face treatment. A total of 502 responses were collected. Further, a three-step hierarchical regression analysis was conducted using SPSS Windows software version 25.0. Results: The study showed that 131 out of 502 respondents had experience using non-face-to-face treatment, while 371 did not. The factors that influenced the intention to accept non-face-to-face treatment included the general characteristics of the participants (women, underlying disease), personal factors (usefulness, cost savings, knowledge), and environmental factors (social norms, trust, perceived risk). The model demonstrated an explanatory power of 65%. Conclusion: The results of this study directly show that intention is linked to behavior through the interaction between personal and environmental factors. Further research is needed to explore additional factors influencing the intention to accept non-face-to-face treatment, enabling its effective use in preventing and treating various diseases, including infectious diseases.

• 한국인터넷방송통신학회논문지
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• 제24권1호
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• pp.197-205
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• 2024

자율주행 자동차 개발 및 상용화에 있어서 주행안전도 확보가 가장 중요한 시점에서 이를 위해 전방 및 주행차량 주변에 존재하는 다양한 정적/동적 차량의 인식과 검출 성능을 고도화 및 최적화하기 위한 AI, 빅데이터 기반 알고리즘개발 등이 연구되고 있다. 하지만 레이더와 카메라의 고유한 장점을 활용하여 동일한 차량으로 인식하기 위한 연구 사례들이 많이 있지만, 딥러닝 영상 처리 기술을 이용하지 않거나, 레이더의 성능상의 문제로 짧은 거리만 동일한 표적으로 감지하고 있다. 따라서 레이더 장비와 카메라 장비에서 수집할 수 있는 데이터셋을 구성하고, 데이터셋의 오차를 계산하여 동일한 표적으로 인식하는 융합 기반 차량 인식 방법이 필요하다. 본 논문에서는 레이더와 CCTV(영상) 설치 위치에 따라 동일한 객체로 판단하기에 데이터 오차가 발생하기 때문에 설치한 위치에 따라 위치 정보를 연동할 수 있는 기술 개발을 목표로 한다.

• International Journal of Internet, Broadcasting and Communication
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• 제16권1호
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• pp.106-123
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• 2024

This article takes the talk show "Beast Town" as an example to introduce the overall technical solution, technical difficulties and countermeasures for the combination of cartoon virtual characters and virtual studio technology, providing reference and experience for the multi-scenario application of digital humans. Compared with the live broadcast that combines reality and reality, we have further upgraded our virtual production technology and digital human-driven technology, adopted industry-leading real-time virtual production technology and monocular camera driving technology, and launched a virtual cartoon character talk show - "Beast Town" to achieve real Perfectly combined with virtuality, it further enhances program immersion and audio-visual experience, and expands infinite boundaries for virtual manufacturing. In the talk show, motion capture shooting technology is used for final picture synthesis. The virtual scene needs to present dynamic effects, and at the same time realize the driving of the digital human and the movement with the push, pull and pan of the overall picture. This puts forward very high requirements for multi-party data synchronization, real-time driving of digital people, and synthetic picture rendering. We focus on issues such as virtual and real data docking and monocular camera motion capture effects. We combine camera outward tracking, multi-scene picture perspective, multi-machine rendering and other solutions to effectively solve picture linkage and rendering quality problems in a deeply immersive space environment. , presenting users with visual effects of linkage between digital people and live guests.