• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.687-708
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• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.312-317
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• 2005

Ubiquitous computing has had an effect to politics, economics, society, culture, education and so forth. For effective management of huge Ubiquitous networks environment, various computers which are connected to networks has to decide automatic optimum with intelligence. Currently in many areas, data mining has been used effectively to construct intelligent systems. We proposed a hybrid support vector machine for Ubiquitous data mining which realized intelligent Ubiquitous computing environment. Many data were collected by sensor networks in Ubiquitous computing environment. There are many noises in these data. The aim of proposed method was to eliminate noises from stream data according to sensor networks. In experiment, we verified the performance of our proposed method by simulation data for Ubiquitous sensor networks.

• Laser Solutions
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• v.11 no.3
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• pp.21-24
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• 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.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.1-11
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• 2023

Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.404-409
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• 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
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• v.13 no.2
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• pp.231-243
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• 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
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• v.1 no.4
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• pp.399-404
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• 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.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1059-1065
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• 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
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• v.14 no.1
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• pp.22-31
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• 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.

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.873-882
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• 1998

The parallel database machine approach is currently widely and successfully used. There are four major architectures which are used in this approach: shared-nothing architecture, shared-evertying architecture, shared-disk architecture, and hybrid architecture. In this paper, we use an analytical model to evaluate the performance of these database machine architectures. We define an abstract model for each type of database machine design to obtain performance equatons describing the execution times with respect to the hybrid hash join poeration. Using the performance equations, we evaluate the execution times of the various database machine design models.