AN ANALYSIS OF CURRENT PROBLEMS IN CHINA'S AGRICULTURE DEVELOPMENT: AGRICULTURE, RURAL AREAS AND FARMERS

China is the most populous country in the world. Of its 1.3 billion people, 22% of the world population, about 67% are living in rural areas. Although China is the third largest country in terms of area, the arable land is only 7% of the global amount. With relatively meager endowment, it is undoubtedly a daunting task for the agricultural sector to provide adequate supply to fulfil huge needs for food and other agricultural products. In addition, agriculture development in China confronts with challenges to raise the average income and standard of living of the rural population in the long run. Since China's economic reform was launched in 1978, the "People's Commune" system was dismantled and replaced by the "Household Responsibility" system. Agricultural production has achieved rapid growth and income per capita in the rural area has risen 10 times in 20 years. During this transformation process, a number of serious problems have been emerging in the agricultural sector. They include the diminishing size of the arable land, enlarging of income disparity and stagnating of productivity growth, which have been exacerbated by the population growth and increasing demands for agricultural products. The agricultural sector is also plagued by environmental degradation and confronted by township enterprise development. Furthermore, China's recent accession into the World Trade Organization (WTO) brings more tremendous challenges to its agriculture. This paper is intended to provide a concise analysis of the problems and possible policy options associated with current agriculture development. It reveals that the main problems are market partition, inefficiency in government administration in supply and distribution, and price distortions of agricultural products, originating from China's development strategy of preferred industrialization in the industry sector and urban development. This paper also explores and assesses a few government policy options for the alleviation of these problems. Policy options focus on deepening market-oriented reforms, including price deregulation, market integration and property (land) reforms, which also reflect the requirements of the Agriculture Agreement of WTO. Policy options also focus on improvement of government supported programs in investment and subsidies aimed at boosting productivity, narrowing the inequality of income distribution and easing the barriers for mobility of surplus labor into the industry and service sectors in urban areas.International Development,

Real-Time Machine Learning for Quickest Detection

Safety-critical Cyber-Physical Systems (CPS) require real-time machine learning for control and decision making. One promising solution is to use deep learning to discover useful patterns for event detection from heterogeneous data. However, deep learning algorithms encounter challenges in CPS with assurability requirements: 1) Decision explainability, 2) Real-time and quickest event detection, and 3) Time-eficient incremental learning. To address these obstacles, I developed a real-time Machine Learning Framework for Quickest Detection (MLQD). To be specific, I first propose the zero-bias neural network, which removes decision bias and preferabilities from regular neural networks and provides an interpretable decision process. Second, I discover the latent space characteristic of the zero-bias neural network and the method to mathematically convert a Deep Neural Network (DNN) classifier into a performance-assured binary abnormality detector. In this way, I can seamlessly integrate the deep neural networks\u27 data processing capability with Quickest Detection (QD) and provide real-time sequential event detection paradigm. Thirdly, after discovering that a critical factor that impedes the incremental learning of neural networks is the concept interference (confusion) in latent space, and I prove that to minimize interference, the concept representation vectors (class fingerprints) within the latent space need to be organized orthogonally and I invent a new incremental learning strategy using the findings, I facilitate deep neural networks in the CPS to evolve eficiently without retraining. All my algorithms are evaluated on real-world applications, ADS-B (Automatic Dependent Surveillance Broadcasting) signal identification, and spoofing detection in the aviation communication system. Finally, I discuss the current trends in MLQD and conclude this dissertation by presenting the future research directions and applications. As a summary, the innovations of this dissertation are as follows: i) I propose the zerobias neural network, which provides transparent latent space characteristics, I apply it to solve the wireless device identification problem. ii) I discover and prove the orthogonal memory organization mechanism in artificial neural networks and apply this mechanism in time-efficient incremental learning. iii) I discover and mathematically prove the converging point theorem, with which we can predict the latent space topological characteristics and estimate the topological maturity of neural networks. iv) I bridge the gap between machine learning and quickest detection with assurable performance

Exploration and Comparison of Image-based Techniques for Strawberry Detection

Strawberry is an important cash crop in California, and its supply accounts for 80% of the US market [2]. However, in current practice, strawberries are picked manually, which is very labor-intensive and time-consuming. In addition, the farmers need to hire an appropriate number of laborers to harvest the berries based on the estimated volume. When overestimating the yield, it will cause a waste of human resources, while underestimating the yield will cause the loss of the strawberry harvest [3]. Therefore, accurately estimating harvest volume in the field is important to farmers. This paper focuses on an image-based solution to detect strawberries in the field by using the traditional computer vision technique and deep learning method. When strawberries are in different growth stages, there are considerable differences in their color. Therefore, various color spaces are first studied in this work, and the most effective color components are used in detecting strawberries and differentiating mature and immature strawberries. In some color channels such as the R color channel from the RGB color model, Hue color channel from the HSV color model, \u27a\u27 color channel from the Lab color model, the pixels belonging to ripe strawberries are clearly distinguished from the background pixels. Thus, the color-based K-mean cluster algorithm to detect red strawberries will be exploited. Finally, it achieves a 90.5% truth-positive rate for detecting red strawberries. For detecting the unripe strawberry, this thesis first trained the Support Vector Machine classifier based on the HOG feature. After optimizing the classifier through hard negative mining, the truth-positive rate reached 81.11%. Finally, when exploring the deep learning model, two detectors based on different pre-trained models were trained using TensorFlow Object Detection API with the acceleration of Amazon Web Services\u27 GPU instance. When detecting in a single strawberry plant image, they have achieved truth-positive rates of 89.2% and 92.3%, respectively; while in the strawberry field image with multiple plants, they have reached 85.5% and 86.3%

Resource Optimization for Air Mobility Under Emergency Situations

This project aims to improve air traffic management in emergencies. We first developed a GRU neural network to forecast weather-related airport capacity constraints using historical data, underscoring the value of real-time data analysis. We then optimized emergency evacuation air travel using Particle Swarm Optimization, demonstrating the ability to quickly aggregate evacuation flight resources cost-effectively. Finally, we provided a hybrid model combining a genetic algorithm with a neural network for evacuation planning, we show that neural network can be integrated accelerate genetic algorithms for efficient and performance assured system optimization

Online Control for Linear Dynamics: A Data-Driven Approach

This paper considers an online control problem over a linear time-invariant system with unknown dynamics, bounded disturbance, and adversarial cost. We propose a data-driven strategy to reduce the regret of the controller. Unlike model-based methods, our algorithm does not identify the system model, instead, it leverages a single noise-free trajectory to calculate the accumulation of disturbance and makes decisions using the accumulated disturbance action controller we design, whose parameters are updated by online gradient descent. We prove that the regret of our algorithm is $\mathcal{O}(\sqrt{T})$ under mild assumptions, suggesting that its performance is on par with model-based methods

Pengaruh Kualitas Pelayanan Terhadap Kepuasan Pelanggan Dan Konsekuensinya Pada Loyalitas (Studi Pada Obyek Wisata Di Kabupaten Malang)

Studi ini meneliti kepuasan wisatawan yang mengunjungi obyek wisata yang ada di Kabupaten Malang dengan menggunakan konsep dasar Swedish Customer Satisfaction Barometer (SCSB). Tujuan penelitian untuk menganalisis pengaruh langsung kualitas layanan (service quality) terhadap kepuasan wisatawan domestik (customer satisfaction), menganalisis pengaruh langsung harapan konsumen (customer expectation) terhadap kepuasan wisatawan domestik (customer satisfaction), dan menganalisis pengaruh langsung kepuasan konsumen (customer satisfaction) terhadap loyalitas konsumen (customer loyalty) wisatawan domestik. Sampel penelitian adalah wisatawan domestik yang berkunjung ke objek wisata (Pantai Sendang Biru, Pantai Ngliyep dan Pantai Bale Kambang), yaitu sebanyak 150 responden. Teknik analisis data yang digunakan adalah Structural Equation Modelling (SEM) dengan menggunakan bantuan program AMOS. Hasil penelitian menunjukkan bahwa ada pengaruh langsung antara kualitas layanan dan kepuasan pelanggan, tidak ada pengaruh yang signifikan anatara harapan dengan kepuasan pelanggan, ada pengaruh langsung antara kepuasan pelanggan dengan loyalitas konsumen. Variabel kualitas layanan yaitu reliability dan emphaty memiliki pengaruh yang paling besar terhadap kepuasan pelanggan sedangkan responsiveness, assurance, dan tangible memilki pengaruh yang cukup signifikan