• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권2호
• /
• pp.687-708
• /
• 2017

The machine-to-machine (M2M) communication is featured by tremendous number of devices, small data transmission, and large uplink to downlink traffic ratio. The massive access requests generated by M2M devices would result in the current medium access control (MAC) protocol in LTE/LTE-A networks suffering from physical random access channel (PRACH) overload, high signaling overhead, and resource underutilization. As such, fairness should be carefully considered when M2M traffic coexists with human-to-human (H2H) traffic. To tackle these problems, we propose an adaptive Slotted ALOHA (S-ALOHA) and time division multiple access (TDMA) hybrid protocol. In particular, the proposed hybrid protocol divides the reserved uplink resource blocks (RBs) in a transmission cycle into the S-ALOHA part for M2M traffic with small-size packets and the TDMA part for H2H traffic with large-size packets. Adaptive resource allocation and access class barring (ACB) are exploited and optimized to maximize the channel utility with fairness constraint. Moreover, an upper performance bound for the proposed hybrid protocol is provided by performing the system equilibrium analysis. Simulation results demonstrate that, compared with pure S-ALOHA and pure TDMA protocol under a target fairness constraint of 0.9, our proposed hybrid protocol can improve the capacity by at least 9.44% when ${\lambda}_1:{\lambda}_2=1:1$and by at least 20.53% when ${\lambda}_1:{\lambda}_2=10:1$, where ${\lambda}_1,{\lambda}_2$ are traffic arrival rates of M2M and H2H traffic, respectively.

• 한국지능시스템학회논문지
• /
• 제15권3호
• /
• pp.312-317
• /
• 2005

유비쿼터스 컴퓨팅 환경은 정치, 경제, 사회, 문화, 교육 등 대부분의 분야에 많은 영향을 주고 있다. 인터넷에 비해 훨씬 거대한 유비쿼터스 네트워크 환경이 효과적으로 운영되기 위해서는 네트워크에 접속한 다양한 컴퓨터들이 스스로 지능을 가지고 주어진 상황에서 최적의 의사결정을 할 수 있어야 한다. 현재 많은 분야에서 데이터 마이닝은 지능형 시스템 구축을 위한 효과적인 분석도구로 사용되고 있다. 지능화된 유비쿼터스 컴퓨팅 환경의 구현을 위한 유비쿼터스 데이터 마이닝을 위하여 본 논문에서는 변형된 Support Vector Machine 기법을 제안하였다. 유비쿼터스 컴퓨팅 환경에서 상당 부분의 데이터가 센서를 통하여 수집된다. 센서 네트워크를 통하여 수집된 데이터는 상당부분 잡음을 포함한 데이터이다. 제안 기법은 특히 센서 네트워크를 통한 스트림 데이터의 잡음을 제거하는 데 목적을 두고 있다. 본 논문의 실험에서는 유비쿼터스 센서 네트워크를 나타내는 다양한 분포로부터 시뮬레이션 데이터를 생성하여 제안 방법의 성능 평가를 수행하였다.

• 한국레이저가공학회지
• /
• 제11권3호
• /
• pp.21-24
• /
• 2008

Hybrid laser welding technology is a good process to reduce a thermal distortion and increase the productivity. However, it requires a high investment and a massive modification of the fabrication line such as a gantry system, milling machine for the edge preparation, high power laser system and weld machine. Therefore the development of an economical laser welding system is a crucial point to apply this system in shipbuilding yard. In this study, a portable hybrid laser welding carriage was developed for I-butt joint without edge milling. It is expected that the carriage type system could reduce investment cost.

• 한국컴퓨터정보학회논문지
• /
• 제28권11호
• /
• pp.1-11
• /
• 2023

유방암은 전 세계적으로 여성들 대다수에게 가장 두려워하는 질환이다. 오늘날 데이터의 증가와 컴퓨팅 기술의 향상으로 머신러닝(machine learning)의 효율성이 증대되어 암 검출 및 진단 등에 중요한 역할을 하고 있다. 딥러닝(deep learning)은 인공신경망(artificial neural network, ANN)을 기반으로 하는 머신러닝 기술의 한 분야로 최근 여러 분야에서 성능이 급속도로 개선되어 활용 범위가 확대되고 있다. 본 연구에서는 유방암 분류를 위해 전이학습(transfer learning) 기반 DNN(Deep Neural Network)과 SVM(support vector machine)의 구조를 결합한 DNN-SVM Hybrid 모형을 제안한다. 전이학습 기반 제안된 모형은 적은 학습 데이터에도 효과적이고, 학습 속도도 빠르며, 단일모형, 즉 DNN과 SVM이 가지는 장점을 모두 활용 가능토록 결합함으로써 모형 성능이 개선되었다. 제안된 DNN-SVM Hybrid 모형의 성능평가를 위해 UCI 머신러닝 저장소에서 제공하는 WOBC와 WDBC 유방암 자료를 가지고 성능실험 결과, 제안된 모형은 여러 가지 성능 척도 면에서 단일모형인 로지스틱회귀 모형, DNN, SVM 그리고 앙상블 모형인 랜덤 포레스트보다 우수함을 보였다.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2004년도 춘계학술대회 논문집
• /
• pp.404-409
• /
• 2004

To maximize the productivity in machining molds and dies, machine tools should operate at high speeds. During the high speed operation of moving frames or spindles, vibration problems are apt to occur if the machine tool structures are made of conventional steel materials with inferior damping characteristics. However, self-excited vibration or chatter is bound to occur during high speed machining when cutting speed exceeds the stability limit of machine tool. Chatter is undesirable because of its adverse effect on surface finish, machining accuracy, and tool life. Furthermore, chatter is a major cause of reducing production rate because, if no remedy can be found, metal removal rates have to be lowered until vibration-free performances is obtained. Also, the resonant vibration of machine tools frequently occurs when operating frequency approaches one of their natural frequencies because machine tools have several natural frequencies due to their many continuous structural elements. However, these vibration problems are closely related to damping characteristics of machine tool structures. The polymer concrete has high potential for machine tool bed due to its good damping characteristics with moderate stiffness. This paper presents the use of polymer concrete and sandwich structures to overcome vibration problems. Also, co-cure bonding method for functional part mounting was exhibited experimentally, by which manufacturing time and cost for polymer concrete bed will be remarkably reduced.

• International Journal of Internet, Broadcasting and Communication
• /
• 제13권2호
• /
• pp.231-243
• /
• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Journal of international Conference on Electrical Machines and Systems
• /
• 제1권4호
• /
• pp.399-404
• /
• 2012

In this paper, a stator-separated axial flux-switching hybrid excitation synchronous machine (SSAFHESM) is presented, of which the structure and operational principle are introduced. The magnetic field distribution under different excited currents is analyzed, and some characteristics including flux-linkage, EMF and field control ability are studied by finite element analysis (FEA). Tests are carried out on a 12/10-pole prototype machine to validate the analysis results, and an excellent agreement is obtained.

• 한국멀티미디어학회논문지
• /
• 제23권8호
• /
• pp.1059-1065
• /
• 2020

In hospitals, nurses are subjectively determining the urine status to check the kidneys and circulatory system of patients whose statuses are related to patients with kidney disease, critically ill patients, and nursing homes before and after surgery. To improve this problem, this paper proposes a urine spectrum analysis system which clusters urine test results based on a hybrid machine learning model consists of unsupervised learning and supervised learning. The proposed system clusters the spectral data using unsupervised learning in the first part, and classifies them using supervised learning in the second part. The results of the proposed urine spectrum analysis system using a mixed model are evaluated with the results of pure supervised learning. This paper is expected to provide better services than existing medical services to patients by solving the shortage of nurses, shortening of examination time, and subjective evaluation in hospitals.

• Industrial Engineering and Management Systems
• /
• 제14권1호
• /
• pp.22-31
• /
• 2015

In this paper, a two-stage hybrid flow shop problem is considered. Specifically, there exist identical parallel machines at stage 1 and two dedicated machines at stage 2, and the objective of the problem is to minimize makespan. After being processed by any machine at stage 1, a job must be processed by a specific machine at stage 2 depending on the job type, and one type of jobs can have different processing times on each machine. First, we introduce the problem and establish complexity of several variations of the problem. For some special cases, we develop optimal polynomial time solution procedures. Then, we establish some simple lower bounds for the problem. In order to solve this NP-hard problem, three heuristics based on simple rules such as the Johnson's rule and the LPT (Longest Processing Time first) rule are developed. For each of the heuristics, we provide some theoretical analysis and find some worst case bound on relative error. Finally, we empirically evaluate the heuristics.

• 한국정보처리학회논문지
• /
• 제5권4호
• /
• pp.873-882
• /
• 1998

현재 병렬 데이타베이스 컴퓨터가 광범위하고 성공적으로 활용되고 있다. 이의 구조로는 주기억 장치와 디스크를 공유하지 않는 구조, 두가지를 모두 공유하는 구조, 디스크만을 공유하는 구조, 그리고 절충형 구조 등의 네가지 구조가 있다. 이 논문에서는 데이타베이스 컴퓨터 구조의 성능을 비교 분석하기 위하여 데이타베이스 컴퓨터 구조를 추상적인 모형으로 정의하고, 각각의 모형에 대하여 절충형 해쉬 조인 연산의 수행시간을 수식화한 성능식을 구하여 여러 가지 데이타베이스 컴퓨터 구조 모형의 수행시간을 비교 분석한다.