• Advances in nano research
• /
• 제15권6호
• /
• pp.513-520
• /
• 2023

The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.480-483
• /
• 2003

We propose a new framework that aims at multi-purpose image recognition, a difficult task for the conventional rule-based systems. This framework is farmed based on the idea of computer-based learning algorithm. In this research, we introduce the new functions of an additional learning and a knowledge reconstruction on the Fuzzy Inference Neural Network (FINN) (1) to enable the system to accommodate new objects and enhance the accuracy as necessary. We examine the capability of the proposed framework using two examples. The first one is the capital letter recognition task from UCI machine learning repository to estimate the effectiveness of the framework itself, Even though the whole training data was not given in advance, the proposed framework operated with a small loss of accuracy by introducing functions of the additional learning and the knowledge reconstruction. The other is the scenery image recognition. We confirmed that the proposed framework could recognize images with high accuracy and accommodate new object recursively.

• 융합신호처리학회논문지
• /
• 제21권3호
• /
• pp.121-126
• /
• 2020

소리 기반 기계 고장 진단은 기계의 음향 방출 신호에서 비정상적인 소리를 자동으로 감지하는 것이다. 수학적 모델을 사용하는 기존의 방법은 기계 시스템의 복잡성과 잡음과 같은 비선형 요인이 존재하기 때문에 기계 고장 진단이 어려웠다. 따라서 기계 고장 진단의 문제를 딥러닝 기반 이미지 분류 문제로 해결하고자 한다. 본 논문에서 스펙트로그램 이미지를 이용한 CNN 기반 자동화 기계 고장 진단 기법을 제안한다. 제안한 방법은 기계의 결함 시 발생하는 주파수상의 특징 벡터를 효과적으로 추출하기 위해 STFT를 사용하였으며, STFT에 의해 검출된 특징 벡터들은 스펙트로그램 이미지로 변환하여 CNN을 이용해 기계의 상태별로 분류한다. 그 결과는 제안한 방법은 효과적으로 결함을 탐지할 뿐만 아니라 소리 기반의 다양한 자동 진단 시스템에도 효과적으로 활용될 수 있다.

• 한국멀티미디어학회논문지
• /
• 제20권2호
• /
• pp.134-143
• /
• 2017

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

• Journal of Electrical Engineering and Technology
• /
• 제13권6호
• /
• pp.2511-2520
• /
• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• 인터넷정보학회논문지
• /
• 제23권2호
• /
• pp.1-7
• /
• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• International Journal of Computer Science & Network Security
• /
• 제22권12호
• /
• pp.229-238
• /
• 2022

Due to digitization, data has become a tsunami in almost every data-driven business sector. The information wave has been greatly boosted by man-to-machine (M2M) digital data management. An explosion in the use of ICT for farm management has pushed technical solutions into rural areas and benefited farmers and customers alike. This study discusses the benefits and possible pitfalls of using information and communication technology (ICT) in conventional farming. Information technology (IT), the Internet of Things (IoT), and robotics are discussed, along with the roles of Machine learning (ML), Artificial intelligence (AI), and sensors in farming. Drones are also being studied for crop surveillance and yield optimization management. Global and state-of-the-art Internet of Things (IoT) agricultural platforms are emphasized when relevant. This article analyse the most current publications pertaining to precision agriculture using ML and AI techniques. This study further details about current and future developments in AI and identify existing and prospective research concerns in AI for agriculture based on this thorough extensive literature evaluation.

• 한국정밀공학회지
• /
• 제12권6호
• /
• pp.96-104
• /
• 1995

A study for a lathe to machine workpiece with noncircular cross-section is presented. The noncircular cutting is accomplished by controlling radial tool position synchronized with revolution angle of the spindle according to the desired cross-sectional shape. A learning control algorithm is suggested for the tool positioning. The learning law of the algorithm is based on pole-zero cancellation, which guarantees the control stability. The control performances are analyzed and simulated on a numerical computer that the effectiveness of the control algorithm is convinced. The algorithm is tested on a conventional NC-lathe which shows some successful results.

• 자원환경지질
• /
• 제53권4호
• /
• pp.479-489
• /
• 2020

단층은 근원암에서 형성된 석유 가스 등의 탄화수소가 이동하는 통로이자 탄화수소를 가두는 덮개암의 역할을 할 수 있는 지질구조로, 탄화수소가 축적된 저류층을 찾기 위한 탄성파 탐사의 주요 대상 중 하나이다. 하지만 기존의 유사성, 응집성, 분산, 기울기, 단층가능성 등 탄성파 자료의 측면 방향 불연속성을 활용하는 단층 감지 방법들은 전문지식을 갖춘 해석자가 많은 계산 비용과 시간을 투자해야 한다는 문제가 있다. 따라서 많은 연구자들이 단층 해석에 필요한 계산 비용과 시간을 절약하기 위한 다양한 연구를 진행하고 있고, 최근에는 머신러닝 기술을 활용한 연구들이 활발히 수행되고 있다. 단층 해석에는 다양한 머신러닝 기술들 중 서포트백터머신, 다층퍼셉트론, 심층 신경망, 합성곱 신경망 등의 알고리즘이 사용되고 있다. 특히 합성곱 신경망을 활용한 연구는 독자적인 구조의 모델을 사용한 연구뿐만 아니라, 이미지 처리 분야에서 성능이 검증된 모델을 활용한 연구 및 단층의 위치와 주향, 경사 등의 정보를 함께 해석하는 연구도 활발히 진행되고 있다. 이 논문에서는 이러한 연구들을 조사하고 분석하여, 현재까지 단층 위치 및 단층 정보 해석에 가장 효과적인 기술은 영상 처리 분야에서 검증된 U-Net 구조를 바탕으로 한 합성곱 신경망인 것을 확인했다. 이러한 합성곱 신경망에 전이학습 및 데이터 증식 기법을 접목하면 앞으로 더욱 효과적인 단층 감지 및 정보 해석이 가능할 것으로 기대된다.

• 대한임베디드공학회논문지
• /
• 제14권5호
• /
• pp.277-286
• /
• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.