Machine Learning Based Neighbor Path Selection Model in a Communication Network

Yong-Jin Lee 한국인터넷방송통신학회 2021 Journal of Advanced Smart Convergence Vol.10 No.1

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

Deep Machine Learning and Neural Networks: An Overview

Chandrahas Mishra,Dharmendra Lal Gupta 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11

Deep learning is a technique of machine learning in artificial intelligence area. Deep learning is a refined "machine learning" algorithm that surpasses a considerable lot of its forerunners in its capacity to perceive syllables and pictures. As of now Deep learning is a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry items. Neural networks are used to implement the machine learning or to design intelligent machines. In this paper thorough survey to all machine learning paradigms and application areas of deep machine learning and different types of neural networks with applications are discussed.

Prediction of RC T-Beams Shear Strength Based on Machine Learning

Saad A. Yehia,Sabry Fayed,Mohamed H. Zakaria,Ramy I. Shahin 한국콘크리트학회 2024 International Journal of Concrete Structures and M Vol.18 No.5

The contribution of shear resisted by flanges of T-beams is usually ignored in the shear design models even though it was proven by many experimental studies that the shear strength of T-beams is higher than that of equivalent rectangular cross-sections. Ignoring such a contribution result in a very conservative and uneconomical design. Therefore, the aim of this research is to investigate the capability of machine learning (ML) techniques to predict the shear capacity of reinforced concrete T-beams (RCTBs) by incorporating the contribution of the flange. Five machine learning (ML) techniques, which are the Decision Tree (DT), Random Forest (RF), Gradient Boosting Regression Tree (GBRT), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), are trained and tested using 360 sets of data collected from experimental studies. Among the various machine learning models evaluated, the XGBoost model demonstrated exceptional reliability and precision, achieving an R-squared value of 99.10%. The SHapley Additive exPlanations (SHAP) approach is utilized to identify the most influential input features affecting the predicted shear capacity of RCTBs. The SHAP results indicate that the shear span-to-depth ratio (a/d) has the most significant effect on the shear capacity of RCTBs, followed by the ratio of shear reinforcement multiplied by the yield strength of shear reinforcement (), flange thickness (), and flange width (). The accuracy of the XGBoost model in predicting the shear capacity of RCTBs is compared with established codes of practice (ACI 318-19, BS 8110-1:1997, EN 1992-1-2, CSA23.3-04) and existing formulas from researchers. This comparison reinforces the superior reliability and accuracy of the machine learning approach compared to traditional methods. Furthermore, a user-friendly interface platform is developed, effectively simplifying the implementation of the proposed machine-learning model. The reliability analysis is performed to determine the value of the resistance reduction factor (ϕ) that will achieve a target reliability index (= 3.5).

Machine Learning for Object Recognition in Manufacturing Applications

Huitaek Yun,Eunseob Kim,Dong Min Kim,Hyung Wook Park,Martin Byung-Guk Jun 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.24 No.4

Feature recognition and manufacturability analysis from computer-aided design (CAD) models are indispensable technologies for better decision making in manufacturing processes. It is important to transform the knowledge embedded within a CAD model to manufacturing instructions for companies to remain competitive as experienced baby-boomer experts are going to retire. Automatic feature recognition and computer-aided process planning have a long history in research, and recent developments regarding algorithms and computing power are bringing machine learning (ML) capability within reach of manufacturers. Feature recognition using ML has emerged as an alternative to conventional methods. This study reviews ML techniques to recognize objects, features, and construct process plans. It describes the potential for ML in object or feature recognition and offers insight into its implementation in various smart manufacturing applications. The study describes ML methods frequently used in manufacturing, with a brief introduction of underlying principles. After a review of conventional object recognition methods, the study discusses recent studies and outlooks on feature recognition and manufacturability analysis using ML.

Machine Learning Based Neighbor Path Selection Model in a Communication Network

Lee, Yong-Jin The Institute of Internet 2021 International journal of advanced smart convergenc Vol.10 No.1

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

Lessons Learned from Institutionalization of ML (Machine Learning) Supported HR Services in the Existence of Multiple Institutional Logics

김경민,김희선 한국경영정보학회 2023 Asia Pacific Journal of Information Systems Vol.33 No.4

This study explores how an organization has successfully implemented ML-supported HR services to resolve high employee turnover problems in the IT sector. The empirical setting of the research is where contradicting institutional logics exist among technical, HR, and business groups regarding the ML model development and use of the model predictions in HR services. Institutional framework is used to identify the roles of organizational actors and the legitimacy structures in the organizational environments that can shape or constrain the ML led organizational changes. In institutional theories, technology adoption and organizational change are not only constrained by organizational context, but also fostered through organizational actors’ roles and efforts to increase the legitimacy for the change. This research found that when multiple contradicting institutional logics exist, legitimizing the establishment of an enabling environment for multiple logics to reconcile and for the project to move forward is critical. Industry-wide conditions, previous experiences with the pilot ML project, forming a TFT with clearly defined roles and responsibilities, and relevant KPIs are found to legitimize the HR team and the business division to collaborate with the technical personnel to launch ML-supported HR services.

기계학습 데이터세트 구축 공정 표준화에 관한 파일럿 연구

윤종욱 한국인터넷전자상거래학회 2019 인터넷전자상거래연구 Vol.19 No.5

In this study, the processes required to build machine-learning dataset(ML dataset) were standardized or generalized. The target of the standardization process is to include all types of data used for machine learning, covering text, voice, images and video data. To this end, existing literature reviews and case studies on ML dataset construction have been carried out. In addition, various projects on domestic and international ML dataset construction were reviewed to derive a standardized construction process for each type of data. It also presented a summary table of the types of annotations that take up most of the time and cost in the ML dataset construction process and the cost for each task. Results presented in this study may be useful to assist in a more comprehensive understanding, compared to studies such as construction process studies centered on specific types of data previously performed or case studies on specific industries. In addition, the survey results comparing different types of annunciations by data type are assessed to be more likely to be used compared to previous studies. From a practical point of view, the results of this study could provide useful guidance for organizations interested in deploying ML datasets to plan and schedule at the time of their business initiatives.

4차 산업혁명이 한국인 영어 학습자의 기본적 의사소통능력 발달에 미치는 영향에 관한 비판적 검토

김낙훈 ( Kim Rakhun ) 한국멀티미디어언어교육학회 2018 멀티미디어 언어교육 Vol.21 No.3

The purpose of this study is to theoretically evaluate the impact of the Fourth Industrial Revolution (e.g., machine learning (ML) and deep learning (DL)) on English education in South Korea. Few studies have investigated to what extent ML/DL technologies have instructional potential for Korean English learners’ development of English proficiency. To this end, this study deals with the four research issues by extensively reviewing previous literature on computer science and SLA theories. First, this study introduces several well-known concepts and architectures of ML/DL for the following discussions. Second, the study critically examines the opinions of those who claim that recent progress in machine translation can dramatically reduce the need for foreign language learning. Third, this study highlights the significance of basic communicative competence in English learning contexts in South Korea, which―as specified by the components and principles of construction grammar―enables Korean English learners to generate sentence-level utterances without resorting to memorized formulaic expressions. Finally, this study presents three types of English learning applications built upon ML/DL techniques whose validities are evaluated from a perspective of basic communicative competence. As a final remark, this study suggests that ML/DL techniques guided by the principles and components of construction grammar should be applied to Korean English learning contexts as a way to develop their basic communicative competence in English.

사용자와의 협력 플레이를 위한 강화학습 인공지능 프로세스 구축

정원조 한국게임학회 2020 한국게임학회 논문지 Vol.20 No.1

The goal is to implement AI using reinforcement learning, which replaces the less favored Supporter in MOBA games. ML_Agent implements game rules, environment, observation information, rewards, and punishment. The experiment was divided into P and C group. Experiments were conducted to compare the cumulative compensation values and the number of deaths to draw conclusions. In group C, the mean cumulative compensation value was 3.3 higher than that in group P, and the total mean number of deaths was 3.15 lower. performed cooperative play to minimize death and maximize rewards was confirmed. 연구는 MOBA 게임에서 선호도가 낮은 Supporter를 대체하는 인공지능을 강화학습을 이용한 구현을 목표하였다. ML_Agent를 이용해 게임의 규칙, 환경, 관측 정보, 보상 처벌을 구성하였다. DPS 에이전트로 구성된 그룹과, Support 에이전트가 있는 그룹으로 나누어 강화학습을 진행하였다. 결과 데이터인 누적 보상 값, 사망 횟수 바탕으로 결론을 도출하였다. 협력 플레이 그룹이 비교 그룹보다 평균 누적 보상 값이 3.3 더 높게 측정되었으며 사망 횟수 총합 평균은 3.15 낮게 되었다. 이를 바탕으로 죽음을 최소화하고 보상을 최대화하는 협력 플레이를 수행하는 강화학습을 확인할 수 있었다.

RPG 사용자 플레이 관찰학습 기반 가이드 NPC 인공지능 강화학습

정원조 한국게임학회 2022 한국게임학회 논문지 Vol.22 No.5

본 연구는 RPG에서 목표 레벨 달성을 위한 분기 선택 가이드를 해주는 인공지능 구축을 목적으로 한다. 이를 위하여 RPG 환경 구성, 인공지능 에이전트의 행동, 관측, 처벌 및 보상 설계하여 강화학습을 진행했다. 결과 데이터로 평균 레벨, 던전 입장횟수, 던전 클리어 횟수, 사망횟수를 구하였다. 이를 관찰행동 학습량 분류 데이터와 에이전트 명령 이행률 분류 데이터로 추출하여 플레이 타임 대비 비교 검증하였다. 학습이 완료된 인공지능 가이드는 목표 레벨달성까지 13시간 걸렸으며, 대조군인 가이드 이행률 0%는 44시간을 소모 했지만 목표 달성에 실패했다. 이를 바탕으로 RPG에서 인공지능 가이드의 역할 효율을 검증하였다. 본 연구는 게임 내 목표 달성을 위하여 인공지능을 활용한 콘텐츠 가이드 연구 사례로서 향후 인공지능의 협력을 활용한 게임 개발의 자료로 활용되길 희망한다.