PCA Assisted DTCWT Denoising for Improved DOA Estimation of Closely Spaced and Coherent Signals

Performance of standard Direction of Arrival (DOA) estimation techniques degraded under real-time signal conditions. The classical algorithms are Multiple Signal Classification (MUSIC), and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT). There are many signal conditions hamper on its performance, such as closely spaced and coherent signals caused due to the multipath propagations of signals results in a decrease of the signal to noise ratio (SNR) of the received signal. In this paper, a novel DOA estimation technique named CW-PCA MUSIC is proposed using Principal Component Analysis (PCA) to threshold the nearby correlated wavelet coefficients of Dual-Tree Complex Wavelet transform (DTCWT) for denoising the signals before applying to MUSIC algorithm. The proposed technique improves the detection performance under closely spaced, and coherent signals with relatively low SNR conditions. Also, this method requires fewer snapshots, and less antenna array elements compared with standard MUSIC and wavelet-based DOA estimation algorithms

A New Wavelet Threshold Determination Method Considering Interscale Correlation in Signal Denoising

Due to simple calculation and good denoising effect, wavelet threshold denoising method has been widely used in signal denoising. In this method, the threshold is an important parameter that affects the denoising effect. In order to improve the denoising effect of the existing methods, a new threshold considering interscale correlation is presented. Firstly, a new correlation index is proposed based on the propagation characteristics of the wavelet coefficients. Then, a threshold determination strategy is obtained using the new index. At the end of the paper, a simulation experiment is given to verify the effectiveness of the proposed method. In the experiment, four benchmark signals are used as test signals. Simulation results show that the proposed method can achieve a good denoising effect under various signal types, noise intensities, and thresholding functions

Modeling and Simulation for Sterilization Process of Canned Food

食品加工业中的罐头食品蒸汽灭菌过程是一种典型的多阶段间歇式生产过程。针对该过程,从物质守恒和能量守恒两方面,建立起通过热传递而相互关联的灭菌锅系统模型和罐头系统模型,而两者又构成了灭菌系统完整的数学模型。在此模型基础上进行了整个过程的仿真计算。通过对仿真结果与实验数据的对比,验证了所建模型的有效性。该模型为研究该类生产过程的优化控制策略提供了一个有效的模拟和测试平台。In the food industry,the sterilization of canned food is a typical multi-stage batch process.The mathematical models of the retort system and the can system were proposed on the basis of mass and energy conservation.The dynamical model of sterilization process was composed of these two subsystems which were connected by heat transfer.With the simulation of whole model,not only the accuracy of the mathematical model was demonstrated,but also an effective platform for testing was provided for the optimization and controller design for such production processes.国家自然科学基金(61174093); 中央高校基本科研业务费专项资金(2010121047

단기 기상 예측을 위한 기계 학습 기법

학위논문(박사)--서울대학교 대학원 :공과대학 전기·컴퓨터공학부,2020. 2. 문병로.Machine learning is the study of artificial intelligence that automatically generates programs from data. It is distinguished from conventional programming, which needs to write a series of specific instructions directly to perform a specific task. Machine learning is preferred when it is difficult to develop an effective algorithm for given tasks such as natural language processing or computer vision. Traditionally, numerical weather prediction (NWP) has been a prevailing method to forecast weather. The NWP predicts future weather through simulations using mathematical models based on current weather conditions. However, the NWP has some problems: errors in the current observations are amplified as simulation proceeds; spatial and temporal resolutions are limited; and there is a spin-up problem, in which initial forecasts are unreliable while the model attempts to stabilize. An alternative approach is needed to complement NWP on small spatial and temporal scales. Therefore, we propose short-range weather forecast models that employ machine learning techniques appropriate for a given forecasting problem. First, we introduce dimensionality reduction techniques to construct effective forecasting models with high-dimensional input data. As the dimension of input data increases, the amount of time or memory required by machine learning techniques can increase significantly. This phenomenon is referred to as the curse of dimensionality, which can be ialleviated by dimensionality reduction techniques. Dimensionality reduction techniques include feature selection and feature extraction. Feature selection selects a subset of input variables, while feature extraction projects high-dimensional features to a lower dimensional space. The details of correlation-based feature selection, and principal component analysis (PCA) which is a representative feature extraction are provided. We then propose a scheme for precipitation type forecast as an example of meteorological forecasting using dimensionality reduction techniques. This scheme takes 93 meteorological variables as input, and uses feature selection to assemble an effective subset of input variables. Multinomial logistic regression is used to classify precipitation as rain, snow, or sleet. This scheme achieved predictions which are 13 % more accurate than the original forecasts, and feature selection improved the accuracy to a statistically significant level. Second, we present sampling techniques that help predict rare meteorological events. Machine learning algorithms tend to sacrifice performance on rare instances to overall performance, which is referred to as class imbalance problem. To resolve this problem, undersampling reduces the number of common instances. As an example of meteorological forecasting using undersampling, we propose a scheme for lightning forecast. Meteorological variables from European Centre for Medium-range Weather Forecasts provide the input to our scheme, in which an undersampling is used to alleviate the class imbalance problem, and SVMs are used to forecast lightning activities within a particular location and time interval. When the scheme was trained with the original input data, it could not predict any lightning. After undersampling, however, the scheme successfully detected about 38 % of the lightning strikes. Finally, we propose a selective discretization technique that automatically selects and discretizes suitable variables for discretization. Discretization is a preprocessing technique that converts continuous variables into categorical ones. Conventional discretization techniques apply discretization to all variables, which may lead to significant information loss. The selective discretization minimizes information loss by discretizing only variables that have nonlinear relationship with the dependent variable. We suggest a scheme for heavy rainfall forecast as an example of meteorological forecasting using the selective discretization. This scheme takes input from automatic weather stations, and predicts whether or not the heavy rain criterion will be met within the next three hours. The input variables are preprocessed to have a compressed yet efficient representation through the selective discretization and iiPCA. Logistic regression uses the preprocessed data to predict whether or not the heavy rain condition will be satisfied. The selective discretization selectively discretized continuous variables such as date and temperature, contributing to the improvement of predictive performance to a statistically significant level. We present effective machine learning techniques for short-range weather forecast, and provide case studies that apply machine learning to precipitation type forecast, lightning forecast, and heavy rainfall forecast. We combine appropriate techniques to solve each forecasting problem effectively, and the resulting prediction models were good enough to be used for operational forecasting system.기계 학습은 주어진 데이터를 통해 자동으로 프로그램을 생성해내는 기법으로서 인공지능 의 한 분야이다. 특정 업무를 수행하기 위해 일련의 구체적인 명령어를 직접 기입해야만 했던 종래의 프로그래밍과 구분되며, 자연어 처리나 컴퓨터 비전에서와 같이 효과적인 알고리즘을 개발하기 힘든 경우 기계 학습이 선호된다. 전통적으로 기상 예보는 수치 예보 기법을 통해 이루어진다. 수치 예보는 현재의 기상 정 보를 바탕으로 수학적 모델을 이용한 시뮬레이션을 통해 미래의 날씨를 예측한다. 하지만 수치 예보 기법은 초기 자료로 사용한 데이터에 오류가 있을 경우 시뮬레이션을 해나가며 그 오류가 증폭되고, 시공간적으로 비교적 낮은 해상도를 지니고 있으며, 일정 시간이 지나야만 예보가 안정화되기 때문에 국소적이면서 단기적인 기상 예측 문제에는 적합하지 않다. 이를 해결하기 위해 주어진 예측 문제에 적절한 기계 학습 기법을 사용하여 효과적으로 단기 기상 예측을 수행하는 방법들을 제안한다. 첫 번째로, 고차원의 입력 데이터를 가지고 효과적인 예측 모델을 만들기 위한 차원 축소 기법들을 소개한다. 입력 데이터의 차원이 증가함에 따라 기계학습 기법들이 필요로 하는 시간 이나 메모리 요구량이 폭발적으로 증가하는 차원의 저주가 발생하는데, 차원 축소 기술은 이를 완화하기 위한 기법들이다. 차원 축소 기술에는 특징 선택과 특징 추출이 있다. 특징 선택은 전체 입력 인자들 중 일부의 입력 인자들만을 선택하는 반면, 특징 추출은 고차원의 입력 데 이터를 저차원의 공간에 투영한다. 상관 관계 기반의 특징 선택과 대표적인 특징 추출 기법인 주성분 분석이 제시되며, 차원 축소 기술을 사용한 기상 예측 사례로서 강수 유형 예측 모델이 제안된다. 해당 모델은 단기 기상 예보에 포함된 93개의 기상 인자를 입력으로 받아 겨울철 강수 유형을 예측한다. 유효한 입력 인자 집합을 선택하기 위해 특징 선택 기법을 사용하며, 다중 로지스틱 회귀는 선택된 입력 인자들을 이용하여 비, 눈, 그리고 진눈깨비 중 어느 형태로 강수가 발생할 것인지 예측하기 위해 사용된다. 본 예측 모델은 강수유형 예측 정확도를 13 % 이상 개선했으며, 본 모델에서 특징 선택은 통계적으로 유의한 수준으로 정확도를 향상시켰다. 두 번째로, 흔치 않은 기상 이벤트를 예측하는 데에 도움을 주는 샘플링 기법들이 소개된다. 훈련 데이터에 클래스가 불균형하게 분포하는 경우 기계 학습 기법들은 전체 정확도를 높이고자 희귀한 예제들에 대한 예측 성능을 희생하는 경향이 있다. 이러한 클래스 불균형 학습 문제를 해결하기 위해 언더샘플링 기법은 흔한 예제의 숫자를 줄인다. 언더샘플링 기법을 사용한 기상 예측 사례로서 뇌전 예측 모델이 제시된다. 해당 모델은 유럽 중기 예보 센터로부터 단기 기상 예보를 입력으로 받아 뇌전 발생 유무를 예측한다. 클래스 불균형 학습 문제를 해결하기 위해 언더샘플링이 사용되며, 지지 벡터 기계를 사용하여 특정 시간대에 특정 지역에서의 뇌전 발생 유무를 예측한다. 원래의 입력 데이터에서는 뇌전을 하나도 예측하지 못했지만 언더샘플링을 통해 약 38 %의 뇌전을 성공적으로 감지해냈다. 마지막으로, 이산화하기에 적합한 인자를 자동으로 선별하여 이산화하는 선택적 이산화 기법이 소개된다. 이산화는 연속형 변수를 범주형 변수로 변환하는 전처리 기법이다. 종래의 이산화 기법은 모든 변수에 대해 이산화를 적용하는데 이 과정에서 정보 손실은 불가피하다. 선택적 이산화 기법은 종속 변수와 비선형 관계에 있는 변수만을 이산화하여 정보 손실을 최 소화한다. 이러한 선택적 이산화 기법을 사용한 기상 예측 사례로서 집중 호우 예측 모델이 제시된다. 본 모델은 자동 기상 관측 시스템으로부터 입력을 받아 세 시간 이내에 호우 주의보 조건이 충족될 것인지를 예측한다. 입력 데이터는 선택적 이산화 기법과 주성분 분석을 통해 응축된 양질의 정보를 담도록 전처리되고, 로지스틱 회귀는 전처리된 입력 데이터를 이용하 여 호우 주의보 조건이 만족될 것인지 예측한다. 선택적 이산화 기법은 일자나 기온과 같은 인자들을 선택적으로 이산화하여 통계적으로 유의한 수준으로 예측 성능향상에 기여했다. 본 논문은 단기 기상 예보를 위한 효과적인 기계 학습 기법들을 제시하고, 강수 유형, 뇌전, 그리고 집중 호우 예측에 기계 학습을 효과적으로 적용한 사례들을 제공한다. 각 사례에서는 해당 예측 문제를 효과적으로 풀 수 있는 기법들을 조합했으며, 우리가 만든 예측 모델들은 실제 운용 목적으로 사용할 수 있을 정도의 성공적인 예측 품질을 보여주었다.1 Introduction 1 1.1 Machine Learning 1 1.1.1 Data Preprocessing 2 1.1.2 Classification 3 1.2 Meteorological Forecasts 4 1.2.1 Precipitation Types 5 1.2.2 Lightning 5 1.2.3 Heavy Rainfall 6 1.3 Main Contributions . 6 1.4 Organization 8 2 Dimensional Reduction Techniques 9 2.1 Correlation-based Feature Selection 10 2.2 Principal Component Analysis 12 2.3 Case Study: Precipitation Type Forecast 14 2.3.1 Introduction 14 2.3.2 Forecast Model 16 2.3.3 Experiments 26 2.3.4 Discussions 37 3 Sampling Techniques 40 3.1 Undersampling 40 3.2 Oversampling 42 3.3 Case Study: Lightning Forecast 43 3.3.1 Introduction 44 3.3.2 Forecast Model 45 3.3.3 Experiments 54 3.3.4 Discussions 62 4 Discretization Techniques 65 4.1 Selective Discretization 66 4.2 Minimum Description Length Discretization 68 4.3 Case Study: Heavy Rainfall Forecast 70 4.3.1 Introduction 71 4.3.2 Early Warning System 73 4.3.3 Experiments 80 4.3.4 Discussions 92 5 Conclusions 95Docto

Novel control of a high performance rotary wood planing machine

Rotary planing, and moulding, machining operations have been employed within the woodworking industry for a number of years. Due to the rotational nature of the machining process, cuttermarks, in the form of waves, are created on the machined timber surface. It is the nature of these cuttermarks that determine the surface quality of the machined timber. It has been established that cutting tool inaccuracies and vibrations are a prime factor in the form of the cuttermarks on the timber surface. A principal aim of this thesis is to create a control architecture that is suitable for the adaptive operation of a wood planing machine in order to improve the surface quality of the machined timber. In order to improve the surface quality, a thorough understanding of the principals of wood planing is required. These principals are stated within this thesis and the ability to manipulate the rotary wood planing process, in order to achieve a higher surface quality, is shown. An existing test rig facility is utilised within this thesis, however upgrades to facilitate higher cutting and feed speeds, as well as possible future implementations such as extended cutting regimes, the test rig has been modified and enlarged. This test rig allows for the dynamic positioning of the centre of rotation of the cutterhead during a cutting operation through the use of piezo electric actuators, with a displacement range of ±15μm. A new controller for the system has been generated. Within this controller are a number of tuneable parameters. It was found that these parameters were dependant on a high number external factors, such as operating speeds and run‐out of the cutting knives. A novel approach to the generation of these parameters has been developed and implemented within the overall system. Both cutterhead inaccuracies and vibrations can be overcome, to some degree, by the vertical displacement of the cutterhead. However a crucial information element is not known, the particular displacement profile. Therefore a novel approach, consisting of a subtle change to the displacement profile and then a pattern matching approach, has been implemented onto the test rig. Within the pattern matching approach the surface profiles are simplified to a basic form. This basic form allows for a much simplified approach to the pattern matching whilst producing a result suitable for the subtle change approach. In order to compress the data levels a Principal Component Analysis was performed on the measured surface data. Patterns were found to be present in the resultant data matrix and so investigations into defect classification techniques have been carried out using both K‐Nearest Neighbour techniques and Neural Networks. The application of these novel approaches has yielded a higher system performance, for no additional cost to the mechanical components of the wood planing machine, both in terms of wood throughput and machined timber surface quality