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One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behaviour. The most important component used to detect cyber attacks or malicious activities is the intrusion detection system (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defence dataset (CSE-CIC-IDS2018), the latest IDS Dataset in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services). The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area under ROC(Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.03. The proposed system of Artificial Intelligence-based Intrusion detection of botnet attack classification is powerful, more accurate and precise. The novel proposed system can be applied to conventional network traffic analysis, cyber-physical system traffic analysis and also can be applied to the real-time network traffic data analysis.

Taguchi's robust parameter design is an approach to reduce the performance variation of quality characteristics in products and processes. In robust design, the signal-to-noise ratio (SN ratio) was used to find the optimum condition to minimize the variation of quality characteristics as much as possible and bring the average of quality characteristics closer to the target value. In this paper, we propose a simultaneous optimization method based on a linear model of the SN ratio as a method to find the optimal condition of the control factor in case of multi-characteristics. In addition, the proposed method and the existing method were compared and studied by taking actual cases.

Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Taguchi has dealt with having constraints on both the mean and variability of a characteristic (the dual response problem) by combining information on both mean and variability into an SN. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. In this paper we propose a substantially simpler optimization procedure for parameter design to solve the dual response problems without resorting to SN.

본 논문에서는 4세대 이동통신 기술표준인 LTE/LTE-A 시스템에서 기존 실외 기지국인 eNB 에 집중되는 이동통신 트래픽 부하를 경감시키고 실내 사용자에게 만족스런 통신품질을 제공하여 더 많은 가입자를 수용하기 위해 적용되고 있는 초소형 실내 기지국인 펨토셀(FemtoCell)에서 발생할 수 있는 다양한 유형의 핸드오버 문제의 최적화 방법을 제시하였다. LTE/LTE-A에서는 펨토셀의 최적 설치와 운용을 위해 자가구성네트워크(SON) 기술이 제안되고 있으며, 특히 실내 사용자의 펨토셀간 이동시 발생할 수 있는 링크절체(RLF)에 의한 핸드오버 문제의 최소화를 위해 핸드오버 파라미터를 최적화하는 MRO 기능이 적용된다. 펨토셀간의 핸드오버에서는 너무 이른 핸드오버, 너무 늦은 핸드 오버 그리고 잘못된 셀로의 핸드오버의 세 가지 유형의 핸드오버 문제가 발생한다. 본 논문에서는 이 러한 세 가지 유형의 핸드오버에서 발생하는 RLF에 의한 핸드오버 문제를 해결하기 위한 효과적인 핸드오버 파라미터 최적화 방안을 제시하고 분석하였다. 본 논문에서 제안하는 MRO 기법은 단말기 에서 측정된 펨토셀들의 수신신호 강도를 기반으로 서빙셀과 타겟셀의 CIO 값을 적응적으로 조정할 수 있는 최적화 기법이다. 다섯 개의 펨토셀로 구성된 모의실험환경에 대한 성능분석 연구결과 본 논 문에서 제안한 MRO 기법은 이 알고리즘을 적용하지 않은 환경에 비해 약 80% 정도의 성능개선 효 과를 제공한다. 따라서 본 논문에서 제안한 펨토셀의 핸드오버 문제 해결과 최적화를 위한 MRO 기 법은 실내 사용자를 위한 LTE-A시스템의 펨토셀 환경에서 효과적인 RLF 방지와 개선을 위해 적용 될 수 있을 것으로 기대된다.

The Self-Organizing Network(SON) technology has been developed in 3GPP Release 10 and 11 standard, so called the LTE-Advanced system, for automatic configuration, operation and maintenance of indoor and outdoor base-stations. In particular, the Femtocell, also called a HeNB(Home eNB), is tending to be deployed not only to accommodate indoor mobile communication users, but also to diverge heavy traffic load concentrated to the outdoor base-station(eNB). The Femtocell is connected to the indoor wired communication networks such as Internet. In such LTE/LTE-A Femtocell systems, three types of handover problems such as too late handover, too early handover and to wrong cell handover problems may occur due to inappropriate handover parameters configuration and its resulting radio link failure(RLF). In this paper, a Mobility Robustness Optimization(MRO) algorithm is proposed to tackle the three handover problems in the LTE-A Femtocell systems. Simulation results show that the proposed MRO algorithm is capable to improve such handover problems up to 80% by optimizing handover parameters automatically. The proposed MRO algorithm will play an important role in providing better communication services to the users in the LTE-A indoor environment where many Femtocells are deployed.

A study to analyze and solve problems of plastic injection molding experiment has presented in this paper. We have taken Taguchi's parameter design approach, specifically orthogonal array, and determined the optimal levels of the selected variables through analysis of the experimental results using S/N ratio.

비트코인은 정부나 금융기관에 의존되어 있지 않은 전자 거래를 지향하며 만들어진 peer-to-peer 방식의 암호화폐이다. 비트코인은 최초 발행 이후 거대한 블록체인 금융 시장을 생성했고, 이에 따라 기계 학습을 이용한 비트코인 가격 데이터를 예측하는 연구들이 활발해졌다. 그러나 기계 학습 연구의 비효율적인 Hyper-parameter 최적화 과정이 연구 진행에 있어 비용적인 측면을 악화시키고 있다. 본 논문은 LSTM(Long Short-Term Memory) 층을 사용하는 비트코인 가격 예측 모델에서 가장 대표적인 Hyper-parameter 중 Timesteps, LSTM 유닛의 수, 그리고 Dropout 비율의 전체 조합을 구성하고 각각의 조합에 대한 예측 성능을 측정하는 실험을 통해 정확한 비트코인 가격 예측을 위한 Hyper-parameter 최적화의 방향성을 분석하고 제시한다.

Bitcoin is a peer-to-peer cryptocurrency designed for electronic transactions that do not depend on the government or financial institutions. Since Bitcoin was first issued, a huge blockchain financial market has been created, and as a result, research to predict Bitcoin price data using machine learning has been increasing. However, the inefficient Hyper-parameter optimization process of machine learning research is interrupting the progress of the research. In this paper, we analyzes and presents the direction of Hyper-parameter optimization through experiments that compose the entire combination of the Timesteps, the number of LSTM units, and the Dropout ratio among the most representative Hyper-parameter and measure the predictive performance for each combination based on Bitcoin price prediction model using LSTM layer.

본 논문에서는 타겟의 RCS Calibration의 정확도를 향상시키기 위해 Time-Gating 조건에 대한 최적화 기법을 제안하였다. Matlab 기반의 코드를 이용하여 RCS Calibration의 최적화 코드를 개발하였으며, 그 조건을 찾기 위해 S-Parameter 신호 처리 알고리즘을 제시하였다. 안테나와 Far-field 조건을 만족한 거리에 있는 타겟 을 두고 측정실험을 진행하였다. 제안하는 신호 최적화 방법의 핵심은 실험에서 측정된 시간 도메인 S-Parameter 값을 확인하여 폭과 레퍼런스 포인트를 조절하는 것이다. 그 방법의 적용 과정을 본 논문에서 서술하였으며, 시간 축에서의 S21의 신호를 확인하여 타겟의 위치를 기준으로 한 Time-Gating 최적화를 진행하였다. 결과적으로 게 이팅 최적화 코드를 적용하였을 때 최적화가 진행되지 않았을 경우와 비교하여 약 12배만큼 정의된 에러 값이 감 소함을 확인할 수 있었다.

In this paper, an optimization technique for time-gating conditions is proposed to improve the accuracy of target RCS calibration. An RCS calibration optimization code was developed using Matlab-based code, and an S-Parameter signal processing algorithm was presented to find the condition. The measurement experiment was conducted with the antenna and the target at a distance that satisfies the far-field condition. The signal optimization method is to check time domain S-parameter value measured in the experiment and adjust the width and reference point. The process of applying the method was described in this paper, and time-gating optimization was performed based on the position of the target by checking the signal of S21 on the time axis. As a result, when the gating optimization code was applied, it was confirmed that the defined error value was reduced by about 12 times compared to the case where optimization was not performed.

LiDAR는 자율 주행뿐만 아니라 다양한 산업 현장에 적용되어 대상의 크기와 거리를 측정 하는 데 사용되고 있다. 이에 더하여 이 센서는 반사된 빛의 양을 바탕으로 반사 강도 영상 또한 제공한다. 이는 측정 대상의 형상에 대한 정보를 제공하여 센서 데이터 처리에 긍정적인 효과를 일으킨다. LiDAR는 고해상도가 될수록 높은 성능을 보장하지만 이는 센서 비용의 증 가를 야기하는데, 이 점은 반사 강도 영상에도 해당된다. 높은 해상도의 반사 강도 영상을 취득 하기 위해서는 고가의 장비 사용이 필수적이다. 따라서 본 연구에서는 저해상도의 반사 강도 영상을 고해상도의 영상으로 개선하는 인공지능을 개발하였다. 이를 위해서 본 연구에서는 최 적의 초해상화 신경망 모델을 위한 파라미터 분석을 수행하였다. 또한, 초해상화 알고리즘을 2,500여 장의 반사 강도 영상에 적용하여 훈련과 검증을 하였다. 결과적으로 반사 강도 영상의 해상도를 향상시켰다. 바라건대 본 연구의 결과가 향후 자율 주행 분야에 적용되어 주행환경 인식과 장애물 탐지 성능 향상에 기여할 수 있기를 기대하는 바이다.

LiDAR is used in autonomous driving and various industrial fields to measure the size and distance of an object. In addition, the sensor also provides intensity images based on the amount of reflected light. This has a positive effect on sensor data processing by providing information on the shape of the object. LiDAR guarantees higher performance as the resolution increases but at an increased cost. These conditions also apply to LiDAR intensity images. Expensive equipment is essential to acquire high-resolution LiDAR intensity images. This study developed artificial intelligence to improve low-resolution LiDAR intensity images into high-resolution ones. Therefore, this study performed parameter analysis for the optimal super-resolution neural network model. The super-resolution algorithm was trained and verified using 2,500 LiDAR intensity images. As a result, the resolution of the intensity images were improved. These results can be applied to the autonomous driving field and help improve driving environment recognition and obstacle detection performance

In this paper, a study was conducted on the methodology for learning the parameters of a neural network using an evolutionary algorithm such as a particle swarm optimization algorithm. The possibility of using the particle swarm optimization algorithm for deep learning was analyzed, and various methods were considered for practical use.

The parameters of support vector machine have a great influence on the learning ability and generalization ability, so an improved ant colony optimization algorithm is proposed to optimize the parameters of SVM, then an optimized SVM classifier (IMACO-SVM) is proposed for data classification. In the IMACO-SVM, the adaptive adjustment pheromone strategy is used to make relatively uniform pheromone distribution and the improved pheromone updating method is used to submerge the heuristic factor by the residual pheromone information, in order to effectively solve the contradiction between expanding search and finding optimal solution. The selection of parameters of the SVM is regarded as a combination optimization of parameters in order to establish the objective function of combination optimization. The improved ACO algorithm with good robustness and positive feedback characteristics and parallel searching is used to search for the optimal value of objective function. In order to validate the classification effectiveness of the IMACO-SVM algorithm, some experimental data from the UCI machine learning database are selected in this paper. The classification results show that the proposed IMACO-SVM algorithm has higher classification ability and classification accuracy.

A concave disc copying icebreaking snow sweeper has been developed for improving the work efficiency and clearing free rate of solid ice and snow, and meeting the rapidity and efficiency of solid snow removal. The machine can simultaneously complete five functions of crush, chop, lifting, scraping and pushing, which also can realize independent servo-copying and self-excited vibrations reduction drag avoidance functions. The structure and working principle of the concave disc copying icebreaking snow sweeper have been introduced. The optimal combination of operating parameters impact on the clearing free rate and efficiency has been determined. The combinatorial optimization test methods of quadratic regression orthogonal rotation center was adopted, the traveling speed, digging depth of the snow, the blade angle of concave, the travel angle of concave blade are made as impacting factors, snow resistance, the clearing free rate, the maximum volume of removing snow pack as the objective function, optimizing experimental studied on the impact of performance of the snow machine operating parameters. The results showed that: the effect of copying and crossing obstacle in machine is good, effectively solve the problem of low clearing free rate of solid ice, while the efficiency of snow removal has been improved. According to the principle of objective function: the smaller snow resistance, the higher clearing free rate, the maximum volume of snow pack, making each factor level interval as constraint optimization, when the snow resistance is smaller than 820N, the clearing free rate is greater than 96%, the maximum volume of snow pack is smaller than 612mm3, the traveling speed ranges from 6km/h to 9.7km/h, the blade angle of concave blade is 19~ 21°, the travel angle of concave blade is 12°~14°. The above operating parameters are the optimal combination of snow machines.

The vibration signal is nonstationary and it is difficult to acquire the sample with typical fault. An improved ACO algorithm based on adaptive control parameters is introduced into SVM model to propose a new fault diagnosis (IMASFD) method in this paper. In the IMASFD method, the EMD method is used to decompose fault vibration signal into IMF components, the energy of IMF components is selected to construct the fault feature vectors. Then the adaptive controlling pheromone strategy, adaptive controlling stochastic selection threshold strategy and dynamic evaporation rate strategy are used to improve the basic ACO algorithm. The improved ACO algorithm is used to optimize the parameters of SVM model in order to obtain the optimal values of parameter combination in the SVM model. And a new fault diagnosis (IMASFD) method is proposed. Finally, the proposed IMASFD method is applied to the test data from bearing data center of CWRU. The experimental results show that the proposed method can accurately and effectively realize high precision fault diagnosis of rolling bearing, and has strong robustness and generalization ability, provides an effective method for realizing fault diagnosis of rolling bearing.

IRIS flower data is a class of multi variable data set, which is widely applied in data classification. This paper aims at the parameter optimization problem of least squares support vector machine (LS-SVM) in data classification, an improved particle swarm optimization(IMPSO) algorithm is introduced into the LS-SVM model for improving the learning performance and generalization ability of LS-SVM model. A new data classification method based on IMPSO algorithm and LS-SVM (IMPSO-LS-SVM) model is proposed. First, the numbers of current iteration and population are added into the control strategy of adaptive adjustment inertia weight in order to improve the performance of inertia weight of PSO algorithm. Then the IMPSO algorithm is used to search the optimal combination values of the parameters of kernel function for obtaining the IMPSO-LS-SVM. Finally, the training samples are used to comprehensively train the IMPSO-LS-SVM, and the best large-scale data classification model is constructed. The IRIS flower data is used to validate the effectiveness of the IMPSO-LS-SVM model. The result indicates that the IMPSO algorithm can effectively search the optimal combination values of the parameters, and the proposed data classification model has better generalization performance, faster training speed and higher classification precision.

In terms of damping low frequency oscillation, power system stabilizer(PSS) plays a very key role. However, in terms of PSS capability exploitation, it is quite important to select the suitable parameters assignment. Different from traditional optimization algorithms with eigenvalue analysis based and with system damping ratio as the aim function, in this paper a new PSS parameters method based on the Polymorphic Bacterial Chemotaxis(PBC) algorithm is presented for PSS parameters optimization. According to the time multiplied absolute error integral criterion (ITAE criteria), this paper chooses the ability of tracking a given value with minimum error of system output as the objective function. Thus the whole process of the disturbed system is considered. Finally, Eigenvalue analysis and nonlinear time-domain simulation are conducted to verify the feasibility and effectiveness of the proposed method.

In recent years, data mining techniques such as neural networks, support vector Regression have been applied extensively to the task of predicting financial variables. As influenced by various factors, the volatility of stock shows a non-linear characteristic, which demonstrates that the forecasting is a non-linear problem. Support vector regression (SVR) is proven to be useful in dealing with non-linear forecasting problems in recent years. The key point in using SVR for forecasting is how to determine the appropriate parameters. An improved Artificial Neural Networks(ANN) algorithm is used to optimize the parameter set of (C, σ), which influences the performance of this model directly. By doing so, this model can deal with the nonlinearity and multi-factors of volatility, and ensure stability and accuracy of support vector machine based regression. Finally, we study a case with the satisfactory result by the SPA test which is showing that this model is more accurate than other models, which guarantees its application.

The SSGAMLP(Small Set Genetic Algorithm Multilayer Perceptron) model helps individual evolution by group evolution. With respect to the MLP, it has better generalization, it can get unknown feature expressions of more possibilities. The model still exist many problems need to be solved. The number of nodes in the hidden layers and the population size of MLP has a great influence on the performance of SSGAMLP. So this paper focuses on the optimization of that two parameters on SSGAMLP. In this paper, the models of several different experiments are designed. By comparing the experimental data, the relationship between the parameter selection and the model performance is obtained.

The objective of this paper is to optimize the parameters of non-coherent detectors such as coherent and non-coherent integration number for various non-coherent detectors such as square law detector, linear detector and logarithmic detector in weibull clutter environment for Ultra Wide Band Short Range Radar in Automotive applications. The detection performance of the detectors is analyzed for fixed false alarm probability of 0.001 and simulation has been done in order to verify it.