A Recent Trend in Individual Counting Approach Using Deep Network

In video surveillance scheme, counting individuals is regarded as a crucial task. Of all the individual counting techniques in existence, the regression technique can offer enhanced performance under overcrowded area. However, this technique is unable to specify the details of counting individual such that it fails in locating the individual. On contrary, the density map approach is very effective to overcome the counting problems in various situations such as heavy overlapping and low resolution. Nevertheless, this approach may break down in cases when only the heads of individuals appear in video scenes, and it is also restricted to the feature’s types. The popular technique to obtain the pertinent information automatically is Convolutional Neural Network (CNN). However, the CNN based counting scheme is unable to sufficiently tackle three difficulties, namely, distributions of non-uniform density, changes of scale and variation of drastic scale. In this study, we cater a review on current counting techniques which are in correlation with deep net in different applications of crowded scene. The goal of this work is to specify the effectiveness of CNN applied on popular individuals counting approaches for attaining higher precision results

모델 공정 불일치 상황에서 화학 생물 공정의 데이터 기반 최적화를 위한 개선항 적응법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 화학생물공학부, 2018. 2. 이종민.개선항 적응법은 데이터 기반 최적화 기법의 일종으로 모델 공정 불일치 조건에서도 수렴하는 값이 공정의 최적 필요 조건을 만족한다는 특징이 있다. 이 학위 논문은 개선항 적응법의 화학 및 생물 공정에 대한 적용 과정에서 발생하는 3 가지 문제점에 대한 해결책을 제시한다. 첫 번째, 반복적으로 발생하는 큰 외란에 의한 최적성 상실의 문제는 과거 외란 정보를 이용하여 앞 먹임 결정기를 디자인 함으로써 빠르게 외란에 대처할 수 있다. 이러한 앞 먹임 결정기는 최신 기법인 심층 신경망 기법을 사용하여 구성하였다. 두 번째, fed-batch reactor 공정의 동적 최적화 문제와 같이 조작 변수의 수가 많은 상황에서 목적 함수와 제약 조건의 실험적 구배를 효과적으로 추정하기 위하여 회귀 분석 방법을 적용하는 방법을 제안하였다. 이를 위하여 multiple linear regression (MLR), principle component analysis (PCA), partial least squares (PLS)와 같은 다양한 회귀 분석 방법이 적용되었고, 보수적인 추정을 위한 moving average 업데이트 방법도 제안되어 수렴했을 때의 공정의 최적 필요 조건 만족이라는 특성을 유지함을 증명하였다. 마지막으로, 업데이트에서 발생할 수 있는 infeasible solution과 공정 노이즈를 효과적으로 처리할 수 있는 새로운 형태의 개선항 적응법을 제안하였다. 또한 제안된 새로운 구조의 개선항 적응법이 갖는 노이즈에 대한 강건성과 수렴성, 그리고 수렴했을 때의 최적 필요조건이 만족함을 증명하였다.1. Introduction 26 1.1 Background and motivation 26 1.2 Literature review 28 1.2.1 Real time optimization 28 1.2.2 Optimality loss by model-plant mismatch 32 1.2.3 Methods to overcome the model-plant mismatch 33 1.3 Major contributions of this thesis 42 1.4 Outline of this thesis 44 2. Data-driven optimization via modifier adaptation 45 2.1 Introduction 45 2.2 Satisfaction of necessary conditions of optimality 47 3. Three issues of modifier adaptation 50 3.1 Issue 1: Frequent and large disturbance 50 3.1.1 Design of feedforward decision maker using machine learning and historical disturbance data 50 3.1.2 Illustrative example 70 3.1.3 Run-to-run optimization of bioprocess 82 3.1.4 Concluding remarks 88 3.2 Issue 2: Experimental gradient estimation under noisy and multivariate condition 89 3.2.1 Importance of gradient estimation for the modifier adaptation 89 3.2.2 Motivational example: Run-to-run optimization of bioreactor 91 3.2.3 Conventional experimental gradient estimation 96 3.2.4 Regression based gradient estimation and its application to modifier adaptation 99 3.2.5 Concluding remarks 129 3.3 Issue 3: A novel structure of modifier adaptation for robustness 130 3.3.1 Feasibility and structural robustness 130 3.3.2 Proposition of new structural modifier adaptation 135 3.3.3 Illustrative example 149 3.3.4 Concluding remarks 155 4. Conclusions and future works 156 4.1 Conclusions 156 4.2 Future works 157Docto

Machine learning in dam water research: an overview of applications and approaches

Dam plays a crucial role in water security. A sustainable dam intends to balance a range of resources involves within a dam operation. Among the factors to maintain sustainability is to maintain and manage the water assets in dams. Water asset management in dams includes a process to ensure the planned maintenance can be conducted and assets such as pipes, pumps and motors can be mended, substituted, or upgraded when needed within the allocated budgetary. Nowadays, most water asset management systems collect and process data for data analysis and decision-making. Machine learning (ML) is an emerging concept applied to fulfill the requirement in engineering applications such as dam water researches. ML can analyze vast volumes of data and through an ML model built from algorithms, ML can learn, recognize and produce accurate results and analysis. The result brings meaningful insights for water asset management specifically to strategize the optimal solution based on the forecast or prediction. For example, a preventive maintenance for replacing water assets according to the prediction from the ML model. We will discuss the approaches of machine learning in recent dam water research and review the emerging issues to manage water assets in dams in this paper

Market-based capabilities, perceived quality and firm performance

The historical roots of the marketing concept are traceable to the early 1950s (Drucker, 1954). However, the field of strategic marketing did not begin to bloom until late 1980s and begin 1990s. In this period various scholars begin to develop a better and more precise understanding of the marketing concept, its antecedents and consequences (Kohli and Jaworski, 1990; Narver and Slater, 1990). Some even suggest that the intellectual foundation for today’s strategic marketing starts early 1980s with the writings of Day and Wensley (1983, 1988). During the late 1990s and early 2000s, various critics begin to rebel at the widespread use of present conceptualizations of market orientation.1 In this thesis, we argue that the present market orientation conceptualizations are becoming outdated (after more than 15 years). We use herebyWeiner’s (2000, p. 382) philosophical words, that a marketing: “theory, like a cat or a dog, has a life of about 10-12 years, which is the equivalent of around 70-84 years of human existence. Longevity in part depends on the size of the pet (the bigger the theory, the earlier the demise), its level of activity, breed, and so on. At around the age of 10, the theory begins to weaken, does not see things too well, and is unable to adapt to the new circumstances and to the many obstacles in life. It can remember and account for the distant past better than recent events, and it acts with rigidity.” The diminishing attractiveness of the present conceptualizations of the marketing concept lead some researchers to look for or move off into new directions, such as (1) the market-based capabilities perspective, where market orientation only represents one of the components (Day, 1994), and (2) the strategic orientation construct, where market orientation is also incorporated as a dimension (Gatignon and Xuereb, 1997). The first perspective deals with the classification of market-based capabilities, which suggests a balanced perspective of inside-out and outside-in capabilities (e.g., Day 1994; Mizik and Jacobson 2003; Noble, Sinha and Kumar 2002; Slack and Lewis 2003; Srivastava, Fahey and Christensen 2001; Vargo and Lusch 2004; Zwart and Postma, 1998). Although a number of classifications exists, these models 1 Especially the Nordic Schools (i.e., Gummesson and Gr¨onroos) go rather far in their criticism. largely incorporate market-driven, relationship-driven and supply-chain capabilities as relevant market-based resources. Another perspective that gains popularity in recent years is the strategic orientation model. The strategic orientation direction incorporates variables like customer orientation, competitor orientation, technology orientation and relational orientation. This perspective integrates the classical strategic management literature with that of market orientation. Although we do not claim that the classical market orientation movement begins to fully lose its early enthusiasm, energy and adherents, we believe it is a good time to explore, synthesize, integrate and extend the previously mentioned directions. By doing so, we also provide evidence whether firms with (several) strong marketing capabilities are in a better position to satisfy the needs of their customers and shareholders. To investigate the propositions we use a dyadic approach, data generated from both customers of wholesalers and suppliers/wholesalers. Furthermore, we investigate, using several statistical methods, the effectiveness of attempting to develop several marketing capabilities simultaneously. In short, the primary purpose of this study is theory building, extension of previous research in the field of market orientation and applying several recently proposed statistical methods to further explore the developed frameworks. However, this study is not only useful from the point of view of advancement of science in marketing, but also from the point of view of advancing managerial decision making. The results derived from the developed models and proposed methods form an essential piece of information to improve marketing decisions. This enables (top) managers faced with the problem of how to trade off competing strategic marketing initiatives to further optimize their decision-making process.

Multivariate study of vehicle exhaust particles using machine learning and statistical techniques

This research has examined the application of machine learning and statistical methods for developing roadside particle (number/mass concentrations) prediction models that can be used for air quality management. Data collected from continuous monitoring stations including pollutants, traffic and meteorological variables were used for training the models. A hybrid feature selection method involving Genetic Algorithms and Random Forests was successfully used in selecting the most relevant predictor variables for the models from the variables selected based on their correlation with the PM$_{10}$, PM$_{2.5}$ and PNC concentrations. The study found that the hybrid feature selection can be used with both statistical and machine learning methods to produce less expensive and more efficient air quality prediction models. Among the machine learning models studied the Boosted Regression Trees (BRT), Random Forests (RF), Extreme Learning Machines (ELM) and Deep Learning Algorithms were found to be the most suitable for the predictions of roadside PM$_{10}$, PM$_{2.5}$, and PNC concentrations. The machine learning models performed better than the ADMS-road model in spatiotemporal predictions involving monitoring sites locations. Moreover, they performed much better in predicting the concentrations in street Canyons. The ANN and BRT were found to be suitable for air quality management applications involving traffic management scenarios

Deep learning applied to data-driven dynamic characterization of hysteretic piezoelectric micromanipulators

The presence of nonlinearities such as hysteresis and creep increases the difficulty in the dynamic modeling and control of piezoelectric micromanipulators, in spite of the fact that the application of such devices requires high accuracy. Moreover, sensing in the microscale is expensive, making model feedback the only viable option. On the other hand, data-driven dynamic models are powerful tools within system identification that may be employed to construct models for a given plant. Recently, considerable effort has been devoted in extending the huge success of deep learning models to the identification of dynamic systems. In the present paper, we present the results of the successful application of deep learning based black-boxmodels for characterizing the dynamic behavior of micromanipulators. The excitation signal is a multisine spanning the frequency band of interest and the selected model is validated with semi static individual sinusoidal curves. Various architectures are tested to achieve a reasonable result and we try to summarize the best approach for the fine tuning required for such application. The results indicate the usefulness and predictive power for deep learning based models inthe field of system identification and in particular hysteresis modeling and compensation in micromanipulation applications

Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data

Deep learning sensor fusion in plant water stress assessment: A comprehensive review

Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations