Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of PSO Communication Topologies on Task Matching in Grid Computing

The ability to solve large-scale problems efficiently is one of the intrinsic values of a grid system. To maximize the throughput of the grid system, matching the submitted tasks to suitable resources is essential. This problem has been identified as the task matching problem. The task matching problem has been studied extensively in the past. Among other nature-inspired algorithms, Particle Swarm Optimization (PSO) has been used more commonly for the task matching problem and has shown promising results. Throughout the literature, the global PSO (gbest) has been chosen as the standard topology, while few other works have considered the local best (ring) topology for the task matching problem. As a result, there is a lack of research investigating the effectiveness of other topologies for the task matching problem. This knowledge gap has motivated our research to elucidate the impact of different PSO topologies on the task matching problem. We observed that the fully connected topology performed best (makespan) in many sets of experiments, but only slightly. Additionally, it was evident from the results that the pyramid topology achieved a slight edge over the other topologies in terms of makespan when all experiments were considered. However, each topology worked better on some problems and not as well on other problems. In addition, the population size significantly impacts the balance between the exploration and the exploitation search process.
Analysis and Modeling of Electricity Market for Energy Storage Systems

This paper presents a detailed technical and economic analysis of existing opportunities for energy storage in electricity market with the focus on California Independent System Operator (CAISO) market. Battery Energy Storage Systems (BESS) can be deployed in power grid to improve power quality, stability, and reliability of the system. However, due to the high initial cost of BESS, the optimal operation of this asset is essential. A few electricity markets in the world currently have a viable platform for BESS participation in different markets. However, the significant details and complexity of regulations in these markets as well as lack of technical and mathematical interpretation of the regulations become an obstacle for researchers to come up with optimum solution for control and planning of BESS in electricity market. In this paper, the requirements and the participating process of energy storage in Ancillary Services including spinning reserve and regulation, as well as energy market for both Day Ahead (DA) and Real Time (RT) markets are studied. The outcome of this paper is the compilation of the CAISO electricity market regulations and the mathematical model for each service as well as constraints that are applicable to BESS deployment.
Estimation of Distribution using Population Queue based Variational Autoencoders

We present a new Estimation of Distribution algorithms (EDA) based on two novel Variational Autoencoders generative model building algorithms. The first method, Variational Autoencoder with Population Queue (VAE-EDA-Q), employs a queue of historical populations, which is updated at each iteration of EDA in order to smooth the data generation process. The second method uses Adaptive Variance Scaling (AVS) with VAE-EDA-Q to dynamically update the variance at which the probabilistic model is sampled. The results obtained prove our methods to be significantly more computationally efficient than state-of-the-art algorithms and perform significantly less number of fitness evaluations when tested on benchmark problems such as Trap-k and NK Landscapes. Moreover, we report results of applying our approach successfully to highly complex problems such as Trap 11, Trap 13, and NK Landscapes with neighborhood size K = 8 and K = 10.
Operating Permissioned Blockchain in Clouds: A Performance Study of Hyperledger Sawtooth

With ever more IoT (Internet of Things) and bigdata applications, the emerging blockchain techniques provide fundamental supports to credibly track the transactions of digital assets. Public blockchains, e.g., bitcoin, are often energy-consuming and low efficient. Therefore, an empirical study of operating permissioned blockchains in clouds is urgently needed. In this paper, we study the performance of Sawtooth, a well-known permissioned blockchain platforms from Hyperledger, in cloud environments. Our results provide insights for blockchain operators to optimize the performance of Sawtooth through adjusting the two configuration parameters, i.e., Scheduler and Maximum Batches Per Block. Our approach can be used to test other blockchain platforms.
Private Cloud Storage Platform Design and Implementation Based on the NAS

With the advent of the ambassador and the rapid development of construction of a high-speed network, digital network resource sharing has penetrated into people's daily work, study and life. Data network storage by which many people share resources, becomes an important way of modern information dissemination and preservation. However, the security of the network storage platform has been a concern to users. Therefore, all kinds of private cloud storage platforms emerge, provide the users with a relatively independent space for personal use. After a long period of use, it is found that the traditional private cloud storage platform can reach a certain amount of security, however, the security is only relative to open public network location. At the same time, there are multiple users under instantaneous access protocol congestion, and big-data feedback delays the problem of high interaction nodes. In view of the traditional architectural characteristics and problems of private cloud causes, the paper, based on the NAS, puts forward private cloud storage platform design and implementation method. The agreement is based on the NAS encryption technology, the optimization of multiple access unit, data compression unit to solve the problems existing in the traditional private cloud. The simulation experiments show that the private cloud storage platform based on the NAS method, the design and implementation of a data storage security access point is high, the peak condition good network smooth general characteristic, high data rate of network transmission interaction, and the advantages of small delay.
On Preserving Information in Schema Transformations: A Constructive Perspective

When a knowledge base (called here schema) is restructured into another schema via some mappings, it is expected that the transformed one contains at least the same information as the original. This characteristic of the transformation is commonly referred to as information capacity preservation or schema dominance. In this paper, we complement the existing definitions of information capacity by offering a formal and constructive perspective to test schema dominance and schema equivalence, in the restricted case of first-order schemas related by first-order mappings. Satisfying some sort of schema equivalence property is essential in areas such as database conceptual design and database reverse engineering. We review, through the lens of our approach, the current literature concerning database normal forms and decompositions, and the process of reverse engineering a database schema.
Reliability investigation of Via-bridges for flexible electronics

In this paper we have carried out investigation of reliability of Via-bridge under mechanical bending for application in flexible electronics. Progresses in the flexible electronics field unlocked the fabrication of next-generation devices such as foldable mobile phones, wearables [1] , electronic skin (e-skin) [2] - [4] , textile electronics [5] , flexible energy harvesting/storage [2] , [5] and flexible memory devices [6] , [7] .
TTDM: A Travel Time Difference Model for Next Location Prediction

Next location prediction is of great importance for many location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Unfortunately, due to the time and space complexity, these methods (e.g., Markov models) only use the just passed locations to predict next locations, without considering all the passed locations in the trajectory. In this paper, we seek to enhance the prediction performance by considering the travel time from all the passed locations in the query trajectory to a candidate next location. In particular, we propose a novel method, called Travel Time Difference Model (TTDM), which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Further, we integrate the TTDM with a Markov model via a linear interpolation to yield a joint model, which computes the probability of reaching each possible next location and returns the top-rankings as results. We have conducted extensive experiments on two real datasets: the vehicle passage record (VPR) data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over existing solutions. For example, compared with the Markov model, the top-1 accuracy improves by 40% on the VPR data and by 15.6% on the Taxi data.
Popularity Prediction Caching Using Hidden Markov Model for Vehicular Content Centric Networks

Vehicular Content Centric Network (VCCN) is proposed to cope with mobility and intermittent connectivity issues of vehicular ad hoc networks by enabling the Content Centric Network (CCN) model in vehicular networks. The ubiquitous in-network caching of VCCN allows nodes to cache contents frequently accessed data items, improving the hit ratio of content retrieval and reducing the data access delay. Furthermore, it can significantly mitigate bandwidth pressure. Therefore, it is crucial to cache more popular contents at various caching nodes. In this paper, we propose a novel cache replacement scheme named Popularity-based Content Caching (PopCC), which incorporates the future popularity of contents into our decision making. We adopt Hidden Markov Model (HMM) to predict the content popularity based on the inherent characters of the received interests, request ratio, request frequency and content priority. To evaluate the performance of our proposed scheme PopCC, we compare it with some state-of-the-art schemes in terms of cache hit, average access delay, average hop count and average storage usage. Simulations demonstrate that the proposed scheme possesses a better performance.
Optimization of Virtual Power Plant Electrical Network

The article is devoted to the task of aggregation standalone power systems (SAPS) with distributed energy sources into a single virtual power plant (VPP). Structural topological analysis and the analysis of operating modes stability have been proposed to use for the optimization of the VPP electrical network. The algorithm for the integral assessment of the structural characteristics of the VPP electrical network has been worked out and the coefficients for assessing the operation modes stability of the VPP electrical network have been proposed. The results of the study of the electric network VPP, which aggregates three SAPS with different types of energy sources (transformer substation, gas piston unit, wind power station) are presented. An algorithm for optimizing the VPP electrical network, which aggregates SAPS, has been developed.
Cascaded ROADM Filter Replication and the Effects of Statistical Variations in the Response of Cascaded ROADMs on 100 Gb/s DP-QPSK System Performance

ii Acknowledgements . iv List of Figures . vii List of Tables . xi List of Abbreviations . xii Chapter 1 Introduction . 1 1.1 Research Motivation . 3 1.2 Thesis Objectives and Contributions . 4 1.3 Thesis Organization . 5 Chapter 2 Literature Review . 7 Chapter 3 Filter Replication . 18 3.0 Filter Replication . 18 3.0. 1 Filter Generation . 18 3.0. 2 Luna Optical Vector Analyzer . 20 3.1 Finisar WaveShaper . 25 3.1.1 Overview . 25 3.1.2 Programming . 30 3.1.3 Characterization – Amplitude Response . 31 3.1. 4 Linearity . 36 3.1. 5 Repeatability . 43 3.1. 6 WaveShaper Only Optimization . 45 3.2 Yenista Bandpass Filter Characterization . 55 3.2.1 Micrometer Measurements . 57 3.2. 2 Bandwidth and Center Frequency Drift . 59 3.2. 3 Simulated Yenista Fitting. 67 3.3 WaveShaper and Yenista Bandpass Filter Optimization Process . 76 3.3.1 Bandwidth Narrowing and Center Frequency Shift . 77 3.3.1.1 Objective Function . 81 3.3.2 Passband Impairment . 81 3.3.2.1 Objective Function . 84 3.4 WaveShaper and Yenista Bandpass Filter Optimization Results. 84 3.4.1 Bandwidth Narrowing and Center Frequency Shifts . 85 vi 3.4.2 Passband Impairment . 89 3.4.3 Conclusions . 94 Chapter 4 Results: Bandwidth Narrowing and Center Frequency Shift . 95 4.1 Full-System Simulation Setup . 95 4.2 WaveLogic 3 Experimental Setup . 97 4.3 35 GBaud Simulation Results . 100 4.4 32 GBaud Simulation Results . 108 4.5 WaveLogic 3 Experimental Results . 111 4.6 Conclusions . 121 Chapter 5 Results: Passband Impairments . 124 5.1 Filter Generation . 124 5.1.1 Passband Impairment Parameters . 127 5.2 Experimental Setup . 128 5.3 Experimental Results . 132 5.3.1 Effect of Slope . 150 5.3.2 Changing Ripple Period . 155 5.4 Conclusion . 164 Chapter 6 Conclusions and Future Work . 166 6.1 Conclusions . 166 6.1.1 Filter Replication. 166 6.1.2 Narrowing and Center Frequency Shift . 168 6.1.3 Passband Impairments. 171 6.2 Future Work . 172 References . 175
Modeling of Collaboration Archetypes in Digital Market Places

With everyone collecting and generating value out of data, this paper focus on distributed data trading platforms, digital market places (DMPs). The DMPs can handle the intricacies of data sharing: how, where, and what can be done with the traded data. Here, we represent collaborations among involving parities in DMPs in the form of archetypes and model them with numeric representations for easier manipulation with standard mathematical tools. We also develop an algorithm that aims to map any customer-defined trust-dependent application request into a best-fit infrastructure archetype in a DMP. Also, we propose multiple metrics that allow evaluate and compare competing the DMPs systemically from more dimensions: coverage, extensibility, precision, and flexibility. We demonstrate the effectiveness of these metrics in a concrete use case.
Radar Systems for Ice and Snow Measurements Onboard Manned and Unmanned Aircraft

In this paper, we present a brief overview of the state of the art in radar systems for ice and snow measurements. Emphasis is given to the instrumentation developed at the University of Kansas for multi-band data retrieval at frequencies ranging from 14 MHz to 38 GHz, which is operated onboard manned and unmanned aircraft. The information collected with these systems is used to estimate parameters such as ice thickness, ice surface and bedrock topography, snow cover thickness on sea ice, and annual snow accumulation. We give a summary of recent field programs (including operations out of Punta Arenas, Chile), present sample results, and discuss recent collaborations with Chilean institutions.
An SVR-based Building-level Load Forecasting Method Considering Impact of HVAC Set Points

This paper focuses on a technique to determine the impact of HVAC set point adjustments on building-level electrical load (kW) utilizing Support Vector Regression (SVR) with the minimum possible set of input variables. The paper uses two SVR-based forecasting methods, namely single-step and recursive models. These models are used to forecast hourly electrical loads of a commercial building in Chicago area for the summer period from 8AM to 8PM. The model accuracy is observed to be higher than 95% for hour-ahead load forecasts, and higher than 93% for 12-hour ahead load forecasts. The models presented in the paper can be used to quantify the reduction in electrical load (kW) based on HVAC set point adjustments during peak hours in buildings.
Improving the prediction of cardiovascular risk with machine-learning and DNA methylation data

Classically, the cardiovascular risk of individual is evaluated using phenomenological variables (PV)such as blood pressure, body mass, smoker status, gender, age etc. Here we show that, on prospective study (after 10–15 years)these PV display a poor agreement with case-control samples. We were able to obtain more accurate predictions using both DNA methylation data and PV as input features of a Random Forest model, achieving a ROC-AUC of 0.74. Furthermore, the Random Forest output correlates with the reliability of the predictions producing a ROC-AUC of 0.90 when only the most reliable predictions are taken into consideration.
MM-NAEMO : Multimodal Neighborhood-sensitive Archived Evolutionary Many-objective Optimization Algorithm

In certain multi-objective optimization problems, it may happen that there are two or more different Pareto optimal sets corresponding to the exact same Pareto Front. These are known as multimodal multi-objective optimization problems (MMOPs). Algorithms which are able to generate all the Pareto sets can provide more flexibility in choosing solutions and therefore a possible improvement in the performance. In this work, we build upon the original framework of NAEMO (Neighborhood-sensitive Archived Evolutionary Many-objective Optimization Algorithm) and present Multimodal NAEMO (MM-NAEMO). In MM-NAEMO, the mutation occurs between the points which are local neighbors and each reference line always keeps at least two or more candidate solutions associated with it. Clustering is used to maintain the diversity in the population associated with each reference line, thus providing multiple Pareto sets as output. The algorithm is tested on the test problems of MMO test suite of CEC2019. The experimental results suggest that the proposed algorithm effectively finds majority of the Pareto sets without hampering the Pareto fronts for most of the problems.
An Efficient Genetic Algorithm for Active Space Debris Removal Planning

As the number of debris increases, the space environment becomes crowded and the risk of collision increases. The active debris removal (ADR) technology is an effective measure to suppress the growth of space debris and stabilize the space environment at a safe level. The ADR based a parent spacecraft carrying multi-sub-satellite is one of the most popular methods to de-orbit space debris. The sub-satellites are equipped with different de-orbit devices to capture the debris and move them to lower orbits. One of the key issues have to deal with is that how to plan an efficient strategy for these sub-satellites to visit all the identified debris in one mission at the lowest weighted sum of energy and time. In order to solve the problem, we model the multi-sub-satellite path planning into a multiple traveler salesmen problem (MTSP). Moreover, we propose an improved multi-sub-population parallel genetic algorithm named Route-Break chromosome pairing, it uses an integer coding method and a new crossover-mutation strategy called single chromosome transformation. Each sub-population evolves independently. Finally, we carry out experiments to testify the efficiency of the proposed algorithm. From the experimental results, we can see that the proposed method has a fast convergence speed and a better solution.
Decentralized Voltage Support in a Competitive Energy Market

In order to facilitate the increasing amount of distributed generations and flexible loads, it is envisioned that the future power grid might operate in a more decentralized manner. To this end, this work proposes a decentralized market framework for multiple interconnected microgrids which can exchange energy and provide voltage support. In contrast to the state-of-the-art analysis, we consider both the energy and the voltage support as the commodities which can be exchanged between the microgrids. We use alternating direction method of multipliers (ADMM) as a market clearing mechanism which obtains market clearing prices for the proposed commodities, while considering the grid constraints. Moreover, the proposed mechanism promotes the operation autonomy of the involved entities by safeguarding their information privacy. We test the proposed framework on a modified 144-bus network which is partitioned into 5 microgrids.
A Novel Multi-Mode Adaptive Energy Consumption Minimization Strategy for P1-P2 Hybrid Electric Vehicle Architectures

The presented study aims to propose a new method of driving behavior recognition using a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) in combination with an Energy Consumption Minimization Strategy (ECMS), resulting in a Multi-Mode Adaptive Energy Consumption Minimization Strategy (A-ECMS) for a P1-P2 series parallel Hybrid Electric Vehicle (HEV). Novelty is achieved by focusing on efficient driving mode switching instead of single mode optimization. Therefore, offline optimization was performed over different driving situations to gather different calibrations, which will be utilized in the hybrid propulsion system master controller with the purpose of determining the most fuel-efficient driving mode based on the current driving behavior. A LSTM RNN is used to classify the current driving behavior online based on vehicle speed, acceleration and distance per stop. This paper compares the effect of the proposed method in different driving conditions in order to investigate the benefits and applicability of such a control strategy. Simulations were performed representing a conventional engine-driven vehicle and a hybrid electric vehicle equipped with a P1-P2 series-parallel hybrid propulsion systems. Improvement in fuel consumption against a conventional vehicle of around 52% in average over all driving cycles can be achieved through this approach.
Selecting Superior Candidates from a Suitable Set: A Selective Extraction Algorithm for Accelerating Shapelet Discovery in Time Series Data

A serious challenge that confronts shapelet-based algorithms for time series classification is finding optimal shapelets in a short time. Representative shapelet-discovery algorithms find shapelets by evaluating the qualities of candidates extracted from the subsequences. One of the main difficulties is the large amount of time consumed, due to the excessive number of shapelet candidates. To address the above problem, in this paper we propose a fast and interpretable candidate-extraction algorithm to accelerate the process of shapelet discovery. The proposed algorithm utilizes a time series subclass splitting technique to sample time series dataset first. Then, an IDP (Important Data Point)-based selective-extraction strategy is used to extract shapelet candidates. The generated candidates have significant improvements in quality and reductions in quantity. Furthermore, the shapelet candidates generated are more interpretable. To test the effectiveness of the shapelet candidates generated, we transform the original time series and use an off-the-shelf attribute-selection technique to select optimal shapelets from candidates. We then evaluate the proposed algorithm through extensive experiments. The results demonstrate that the proposed algorithm makes significant improvements in accuracy, compared with baselines. Meanwhile, the time consumption is also greatly reduced.
An Efficient Privacy-Friendly Multi-Hop Data Aggregation Scheme for Smart Grids

In this article, we propose a privacy-friendly data aggregation scheme for smart grids. In our approach, smart meters transmit their usage reports through multi-hop communication, where an aggregation tree is constructed to minimize the aggregation overhead. Security and performance analyses show that our proposed scheme offers better privacy protection for electricity meter reading aggregation as well as computational efficiency, as compared to the existing solutions.