Performance analysis of trajectory compression algorithms on marine surveillance data

The trajectory data of vessel AIS (automatic identification system) has important theoretical and application value for information supporting decisions. However, large sizes lead to difficulties in storing, querying, and processing. To solve the problems of high compression ratio and longtime consumption of the existing online trajectory compression algorithm, an SPM (scan-pick-move) trajectory data compression algorithm added sliding window is proposed. In order to better compress vessel trajectory data regarding compression efficiency, the sliding window is added to the classical SPM algorithm. In order to reduce trajectory data storage space, the maximum offset distance reference trajectory point is used as the criterion of whether the current trajectory point can be compressed. In this paper, the multi-dimensional space-time characteristics of trajectory data, such as distance error, compression ratio and compression time, are selected to evaluate the trajectory compression method from three levels: geometric characteristics, motion characteristics and compression efficiency. Compared with the existing SPM trajectory data compression algorithm, parallel experiments are conducted based on AIS data gathered over the duration of a month in the Japan Osaka Bay. The SPM trajectory compression algorithm added sliding window can significantly reduce the compression time and outperforms other existing trajectory compression algorithms in term of average compression error at high compression strengths. Also, the proposed method has high compression efficiency in the range of commonly used compression thresholds.

With the rapid proliferation of microservices architectures these days, the efficient and fast transfer of large matrix data between services has become a significant challenge. This study presents an analysis aimed at finding solutions to this challenge. The analysis addresses the compression and decompression of large matrix data, focusing on lossless compression algorithms to optimize data transfer without data loss. The study is implemented on an example scenario. This scenario is taken from a project with a microservice architecture. In the example scenario, an image processing service developed in Python programming language generates 640x480 matrix data. After going through a compression algorithm, this data is periodically transferred to a backend service developed in C# programming language. This data is then stored in a database. In the final stage, decompression operations are performed so that this data can be used for reporting. The performance of various compression algorithms in the data compression, database storage and report generation stages is extensively tested. Within the scope of the study, tests were performed using five different compression algorithms (Gzip, Zlib, Deflate, Brotli and Bz2). The results are obtained through performance tests aimed at determining the most optimized end-to-end solution. Analyzing the performance of the compression algorithms on the example scenario, the Brotli algorithm gives the most optimal result in terms of both speed and compression size. This work makes an important contribution to data transfer optimization in microservice architectures and provides a reference for research in this area by presenting the performance analysis of various compression algorithms.

Integrating interlayer ventilation into phase change walls: Comprehensive performance and optimization in summer

Data mining approach to shipping route characterization and anomaly detection based on AIS data

This paper presents an agent-based algorithm for discovering subspace clusters in high dimensional data. Each data object is represented by an agent, and the agents move from one local environment to another to find optimal clusters in subspaces. Heuristic rules and objective functions are defined to guide the movements of agents, so that similar agents(data objects) go to one group. The experimental results show that our proposed agent-based subspace clustering algorithm performs better than existing subspace clustering methods on both F1 measure and Entropy. The running time of our algorithm is scalable with the size and dimensionality of data. Furthermore, an application in stock market surveillance demonstrates its effectiveness in real world applications.

Data compression and processing of image sequences are becoming increasingly important in the era of the information superhighway. This project aims at the development and proof-of-principle of new methods for motion extraction, image sequence compression, and motion analysis. These methods will increase the efficiency of recognition systems and various database applications. The early research into such novel concepts at the forefront of computer vision will benefit LLNL and DOE in all areas associated with archived images and image sequence data. Automated security and surveillance applications are also of special interest in this context. In FY 1997, we started developing a parallel implementation of the face recognition paradigm on the message passing interface (MPI). A parallel implementation is essential to understanding the structure of large image-databases. Our algorithms are now available to interested parties for applications such as scientific data management (SDM). We also are implementing our new algorithms as a growing library of C++ objects. During FY 1997, we focused our research efforts on designing and delivering hardware. In particular, we (1) established the capability to design new retinas and other very-large-scale integrated (VLSI) hardware at LLNL's Institute for Scientific Computing Research (ISCR) and (2) fabricated prototypes through MOSIS, a University of Southern California center for experimental VLSI design. Leveraging our connections to the analog VLSI research community -- particularly connections with the California Institute of Technology, the University of Zurich, and the University of California, San Diego -- we collaborated with Southern Illinois University (SIU) to develop a novel silicon retina with mixed signal processing capabilities. The ITTRACS device delivers the equivalent of 50 x 109 operations (50 GOPS) per second. It performs light detection with logarithmic sensitivity, edge extraction, frame differencing, and centroid calculation at very low power (~100 mW). Our chip exploits properties of the silicon substrate, such as the subthreshold behavior of transistors for computational purposes. At the same time, it avoids high power consumption by clocking capacitance at high frequencies. Our new chip offers advanced features such as the capability to blank out the signal calculation -- e.g., while the platform carrying the sensor updates its position -- and to track several regions independently. We are preparing a record of invention for this chip. This last fiscal year, we also demonstrated a pilot system that tracks a moving object appearing in its field of view. This two-chip system uses a predecessor of our new retina design to detect the position of the moving object. Due to our expertise in silicon retina applications, we obtained prototypes of a new silicon retina and a link decoder from ourCaltech collaborators. These implementations feature a new integrated bus system called "Address Events," which makes feasible the asynchronous communication of similar chips in a multiprocessor system of similar devices. These prototypes are available for testing.

The problem of protecting the GPS signal from interference is addressed. The power inversion algorithm (PI) used in GPS adaptive null-steering arrays is detailed presented. The algorithm based on LCMV rule, which simple to implement and not computationally intensive, is useful because it does not require detailed knowledge about desired-signal structure or arrival angle. In this paper, Comprehensive characteristics attainable with a PI array are derived by sufficient analysis in theory, further more, well experimental results show the fine validity of the proposed characteristics.

Bearings are critical mechanical components in rotating machinery, playing a vital role in system safety and operational continuity. In this study, the Case Western Reserve University (CWRU) bearing dataset is used to perform fault classification using four machine learning algorithms: Random Forest, XGBoost, Support Vector Machine (SVM), and Naive Bayes. Based on statistical features extracted in the time domain, the performance of each model is evaluated using accuracy, precision, recall, and F1-score metrics. The results reveal that Random Forest and XGBoost algorithms achieved superior performance with 95.73% accuracy and 96% in precision, recall, and F1-score. The SVM model, with 93.73% accuracy, stands out as a robust alternative, while the Naive Bayes algorithm shows relatively lower performance with 92.40% accuracy. Additionally, an individual feature-based classification analysis indicates that standard deviation (sd) and root mean square (RMS) features contribute most significantly to model performance. This study provides a comprehensive performance analysis of traditional machine learning algorithms, offering a valuable reference for early and accurate detection of bearing faults.

This thesis conducts three empirical studies on the Chinese A-share initial public offering (IPO) market, specifically, underpricing and short-run underperformance, duration from offering to listing and hot and cold issuing cycles. Before the empirical analysis, the literature is reviewed associated with these three topics. In addition, this thesis presents an overview of the Chinese IPO market which is found to undergo an experiment period from 1984 to 1990, a transition period from 1991 to 1993 and finally steps into a relatively mature stage from 1994. The issuing systems, allocation mechanisms, pricing methods, structure of shareholders are also discussed. The first empirical study analyses underpricing and short-run underperformance of the Chinese A-share IPOs from Mar, 2001 to 2005 when the Approval System is adopted. We find the market adjusted first-day returns average at 93.49% during this period. Then we explore why Chinese IPOs are underpriced so much. The influences of inequality of IPO demand and supply, allocation mechanisms, structure of shareholders, duration to listing and underwriters are discussed respectively. Based on the discussion, we construct an ordinary least squares regression model and find that the underpricing is positively related to this IPO’s turnover ratio and state-owned shares and negatively related to its offering price, issuing size, etc. Meanwhile, this study extends the estimation and compares these IPOs’ short-run underperformance on their 10th, 20th, 30th trading day. We find that the levels of short-run underpricing shrink. Both allocation mechanisms and underwriters can impact the degree of shrinking. The second empirical study estimates the duration from offering to listing of Chinese A-share IPOs issued from 1994 to 2005. We firstly explain the different IPO issuing procedures under the Administrative Authorizing System and the Approval System respectively and then compare the effects of these two issuing systems on the length of this duration. The findings indicate that the waiting time to listing has been shortened greatly after the Approval System is adopted. Secondly, this study emphasizes on exploring endogenous factors related to an issuer itself, such as this issuer’s quality, market sentiment, allocation mechanism, and underwriter, etc. Then a Cox proportional hazard model is employed to examine these factors’ influences on this issuer’s final listing. Further, this study extends the analysis to explore the role of the issuing system and issuing year respectively. Most endogenous factors are found to still be functioning when we take into account the effect of the issuing system, but the effects of underwriter, allocation mechanism, offering price and floatation size diminish in favour of the effect of issuing year. The third empirical study focuses on detecting hot and cold IPO cycles in the Chinese A-share market during 1994-2005 using a Markov regime switching model. We introduce a set of observations to measure IPO monthly activities, which include the number of IPOs, underpricing, market conditions and duration to listing, and thus establish a model to estimate these activities' average performance in hot and cold periods respectively. It is found that a hot period is related with an abundant supply of IPOs, high levels of underpricing, positive market conditions and short waiting time to listing. This study presents the turning points of hot and cold periods for each observation. The cycles detected by the number of IPOs are the benchmark and then these cycles’ robustness is tested by the other observations.

This thesis consists of three empirical studies on what drives stock market dynamics. The first empirical study explores the effect of crude oil price changes on the stock market returns of oil-exporting countries and oil-importing countries as well as those of a number of global stock indices. Using the Ordinary Least Squares (OLS) approach as well as the more robust Quantile Regression (QR) approach to explore the relationship between crude oil and stock market dynamics. The empirical findings suggest that the QR approach provides further insights compared to the OLS approach. For instance, the QR approach is able to identify specific quantiles where a significant relation exists. In particular crude oil price increases tend to have a negative impact on the stock market returns for some oil-exporting countries (such as Mexico, Iraq, Ecuador, and Venezuela) and a positive effect for other oil-exporting countries (such as Brazil and Algeria). However, the OLS approach suggests that these relationships are insignificant at the level of the mean. Overall, the empirical findings confirm that the QR approach can reveal more information about the relationship between crude oil price changes and stock market return across different quantiles of their distribution.The second study explores the extent to which implied volatility extracted from commodity markets and developed stock markets can predict the implied volatility of stock markets in BRICS countries. Using daily data from 2011 to 2016 and employing the newly developed Bayesian Graphical Vector Autoregressive (BGVAR) model of Ahelegbey et al. (2016) which does not suffer from over-parameterization and the identification problems associated with traditional VAR frameworks, this study finds that implied volatilities extracted from global and regional stock markets have a significant predictive power over the implied volatilities in BRICS stock markets. However, the predictive power of implied volatility from commodity markets are significant only in the case of South Africa.The third empirical study analyses the relationship between illiquidity and stock market returns in the G7 and BRICS countries. More specifically, this study explore the extent to which the Amihud (2002) illiquidity measure can improve the explanatory power of three commonly used asset pricing models, namely the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model and the Carhart four-factor model. The empirical analysis is based on 15 years of monthly data on the returns of seven stock portfolios: 100 largest companies (Largest100), small value (S/V), small neutral (S/N), small growth (S/G) stocks, big value (B/V) stocks, big neutral (B/N) stocks, and big growth (B/G) stocks. The findings suggest that incorporating illiquidity as an additional factor results in a significant improvement in the explanatory power of these asset pricing models across several of the sample countries (8 countries in the case of the CAPM and Carhart four-factor model, and 6 countries in the case of the Fama-French three-factor model). For example, in the US adding illiquidity to the CAPM leads to an increase of the goodness of fit by 2.6% in the B/V portfolio, and for the Fama-French three-factor model the goodness of fit increases by up to 3% in all portfolios Moreover, the goodness of fit increases in all portfolios in the US by adding illiquidity to the Carhart four-factor model, with an up to 36% increase in the B/N portfolio.

In order to quantitatively evaluate the effectiveness of college English teaching model reform, an empirical analysis model of college English teaching reform model based on computer big data is proposed. The characteristic quantity of college English teaching model is extracted by multivariate information fusion cluster analysis, and the benefit index data of college English teaching model reform are calculated by principal component game method. The efficiency index of the teaching reform model is treated with fuzzy fusion, and the characteristic clustering and multiple regression analysis are realized, so as to realize the quantitative evaluation of the relevance between the reform of college English teaching mode and the performance. Empirical data analysis shows that this method is used to evaluate the efficiency of college English teaching reform is accurate, and the correlation degree of feature extraction is high, which can effectively reflect the various index parameters of college English teaching reform. The accuracy of empirical analysis is high, and the optimization of teaching reform is promoted.

This paper proposes a method for analyzing comprehensive theoretical digging performance of an excavator based on the convex polytope. The convex polytope is generated by using Newton–Euler’s equations to establish dynamic relationships between the digging capability of the bucket and the driving capability of hydraulic cylinders with the consideration of the excavator tipping and slipping constraints, and is used to identify the excavator’s output capability for digging forces and moments in the bucket force space. A set of indices for theoretically quantifying the digging capability, digging efficiency, as well as the matchability between the manipulator mechanism and the driving capability of hydraulic cylinders are proposed based on the polytope, and they are used to assess the digging trajectory characteristics and the dynamic digging performance of the entire excavator workspace. Two case studies for the excavator’s digging performance assessment and optimal digging trajectory generation are conducted to test and validate this method. The proposed method contributes to comprehensively and deeply understand the design principles of an excavator, and shows the promising application prospect for guiding the design and development of new excavators.

A novel segmented random search based batch scheduling algorithm in fog computing

A stochastic Petri net (SPN) is systematically constructed from a task graph whose component subtasks are statically allocated onto the processor suite of a heterogeneous computing system (HCS). Given that subtask execution times are exponentially distributed an exponential distribution can be generated for the overall completion time. In particular the enabling functions and rate functions used to specify the SPN model provide needed versatility to integrate processor heterogeneity, task priorities, allocation schemes, communication costs, and other factors characteristic of a HCS into a comprehensive performance analysis. The manner in which these parameters are incorporated into the SPN allows the model to be transformed into a testbed for optimization schemes and heuristics. The proposed approach can be applied to arbitrary task graphs including non-series-parallel.

Cloud computing brings numerous advantages that can be leveraged through containerized workloads to deliver agile, dependable, and cost-effective microservices. However, the security of such cloud-based services depends on the assumption of trusting potentially vulnerable components, such as code installed on the host. The addition of confidential computing technology to the cloud computing landscape brings the possibility of stronger security guarantees by removing such assumptions. Nevertheless, the merger of containerization and confidential computing technologies creates a complex ecosystem. In this work, we show how Kubernetes workloads can be secured despite these challenges. In addition, we design, analyze, and evaluate five different Kubernetes deployment models using the infrastructure of three of the most popular cloud providers with CPUs from two major vendors. Our evaluation shows that performance can vary significantly across the possible deployment models while remaining similar across CPU vendors and cloud providers. Our security analysis highlights the trade-offs between different workload isolation levels, trusted computing base size, and measurement reproducibility. Through a comprehensive performance, security, and financial analysis, we identify the deployment models best suited to different scenarios.