Miracle-dz/UniFAR-FTUs huggingface dataset:
BEER analysis of Kepler and CoRoT light curves: discovering binaries and exoplanets

This thesis consists of seven scientific papers that cover the proof-of-concept, the development, and discoveries made through the use of the BEER (BEaming, Ellipsoidal, and Reflection) algorithm for searching for companions in the light curves from the Kepler and CoRoT space telescopes. Paper I presents the detection of the ellipsoidal and the beaming effects in the CoRoT light curve of CoRoT-3, a system of a $22$$M_{\rm Jup}$ brown dwarf orbiting an F star with an orbital period of $4.3$ days. This work served as a proof-of-concept that these effects are detectable in the space light curves of systems with brown-dwarf or planetary secondaries, thus indicating that similar modulations may be detected in the light curves of non-transiting systems. Abridged. The last study, Paper VII, demonstrates the different strengths and utility of the BEER search algorithm. It presents the discovery of four short-period eclipsing binaries in the Kepler light curves, consisting of an A-star primary and a low-mass WD secondary (dA+WD). The systems show BEER phase modulations together with primary and secondary eclipses. These add to the 6 Kepler, and 18 WASP, previously known short-period eclipsing dA+WD binaries. The paper shows that three of the new systems harbor the smallest WDs detected so far in such binaries. These three binaries extend the previously known population to older systems with cooler and smaller WD secondaries, allowing to test binary evolution theories in a parameter region not observed before. The seven papers illustrate the effectiveness of the BEER algorithm in finding both common stellar binaries and rare astrophysical objects. As such, the BEER tool can serve as an important component in the virtual astronomy toolbox for mining the vast astronomical data produced by current and future photometric surveys. Abridged.
A Two-step BP Neural Network Based Machine Learning Algorithm for Solving Unit Commitment

In order to speed up the solution of the unit commitment problem, a two-step BP(Back-Propagation) neural network-based machine learning algorithm is proposed in this paper for performing fast prediction of feasible solutions to the unit commitment. Firstly, this two-step BP neural network is constructed and trained with the training data generated by traditional unit commitment solution method. After that, prediction results of the two-step BP neural network are compared with those of the one-step BP neural network and simulation results of traditional unit commitment solution method in a case study to demonstrate this method’s feasibility and better accuracy. The case study shows that it is feasible to use a two-step BP neural network for fast prediction of unit commitment solutions to support electrical power system optimization, with significantly shorter time compared to the traditional solution method and higher accuracy compared to a one-step BP neural network.
Neuronal electrical activity pattern extracted by 3D clustering and discriminated by a deep CNN

Analyzing the dynamics of neural activity patterns using an electrophysiological approach is important for understanding the basis of information processing in a brain. In this study, we attempted to extract units of neuronal activity representing certain information and to discriminate activity pattern evoked by electrical stimulus and spontaneous activity without stimulation. We applied X-means clustering to the triplet of “2D-spacial coordinates and timestamps” of neuronal electrical spike bursts and the extracted single spatiotemporal neuronal activity pattern was converted into a standardized $8\times 84$ 2D-spatial pattern map. Most 2D-spatial patterns emerged repeatedly during recording time, corresponding to a representative motif. Then we gathered similar 2D-spatial patterns (primary clusters) into one ”pattern repertoire” and attempted to classify these pattern repertoires depending on the stimulus inducing the pattern repertoire by VGG16 deep convolutional neural networks (deep CNN). For that, we prepared two types of $224\times 224$ images by converting the 2D-spatial pattern maps, the spatial information priority neural activity pattern image (SIP-NAP image) and the time information priority neural activity pattern image (TIP-NAP image). As a result, over 80% of high discrimination accuracy was obtained for both types of VGG16 input images, especially 99.7% for TIP-NAP image. It suggested that spatiotemporal features contributing to the discrimination between spontaneous and evoked response activities were extracted by 3-D-crustering. In addition, it is shown that the spatial pattern of neuroelectric activity is able to be discriminated with high accuracy by combining transfer learning and SIP-NAP / TIP-NAP image translation method, even with a small amount of training data.
Wireless Energy Receiving System Combined with Array

This study examines the circuitry and system design of IoT devices to improve their wireless charging efficiencies. Recently, wireless charging applications have become increasingly popular in the IoT field. The main barrier is the low wireless charging efficiency for IoT devices. In this study, we aim to explore the impact of circuit design and component specifications on wireless charging efficiency. This study mainly focuses on the receiver circuit. The RF signals are received by the antenna array and then passed through the impedance matching circuit. Then, they enter the RF -DC rectifier and DC combiner to be superimposed with the DC power. At the final stage, the DC-DC converter stabilizes the output voltage to obtain the targeted charging voltage. In this study, we utilize both the low threshold voltage of the MOSFET devices and the DC combiner scheme to improve the wireless charging efficiency. The simulation results obtained using the T18HVG2 technology at 2.4 GHz are feasible and practical.
Enhancing VRUs Safety with V2P communications: an experiment with hidden pedestrians on a crosswalk

Vehicular Ad hoc Networks (VANETs) are the network that enables communications between road entities, such as vehicles, Road Side Units (RSUs), or Vulnerable Road Users (VRUs). The work presented in this paper aims to provide a methodology to prevent accidents that happen to VRUs' entities, more precisely, to pedestrians. The goal of this safety application use-case is to detect and warn the involved parties of a potentially dangerous situation when a pedestrian crosses a road in a crosswalk where direct line-of-sight detection from both parties is limited or not possible. The warning message is transmitted using Decentralized Environmental Notification Messages (DENMs) using standard communication technologies. Because the different parties may use different communication technologies, a cloud service is used as an intermediary to allow communication between vehicles (using IEEE 802.11p) and pedestrians (using Long-Term Evolution (LTE)). The results show that all the involved entities can be warned in usable time, enough for an action to be taken, such as performing an emergency brake.
Machine Learning for Ischaemic Heart Disease Diagnostic Analysis

Ischaemic Heart Disease (IHD) is a major cause of morbidity and mortality worldwide. Clinical diagnosis involves complex, costly and potentially invasive procedures. To reduce the cost and risk to patients, we present a functioning machine learning (ML) pipeline and developed ML predictive models for IHD diagnosis using a limited number of risk factors in a clinical dataset. We fine-tuned and compared a number of learning algorithms and feature selection methods. The effectiveness of proposed methods was evaluated with quantitative measures and clinician input. The best accuracy of 92.9% was achieved, demonstrating a promising tool for screening IHD patients. Practical issues and data bias when applying ML on medical data are discussed.
Query-Based Retrieval of German Regulatory Documents for Internal Auditing Purposes

Among the countless documents internal auditors face in practice, regulatory documents are by far the most important materials. Auditors need them for every audit engagement to familiarize themselves with the target state of the objects they are auditing. Due to their textual contents, auditors typically work through these documents by hand, which is very time consuming - especially if the documents are complex, structured differently, or are frequently modified. In this context, obtaining relevant regulatory information for an upcoming audit can be challenging, as auditors often have to find the materials they need among hundreds of documents. In this study, we therefore adopted the idea of document retrieval and evaluated it using a text corpus of 709 German regulatory documents, 13 different retrieval variants, and 16 search queries. The results show that especially the combination of a lexical and a semantic search approach provides the best results.
Decoding reach and attempted grasp actions from EEG of persons with Spinal Cord Injury

Brain-computer interfaces (BCIs) could enable persons with cervical spinal cord injury (SCI) to intuitively control assistive motor devices for regaining lost grasping function. Previous studies, mostly performed in non-disabled persons, have already shown that complex upper limb movements can be decoded from the low-frequency time domain of the electroencephalogram (EEG). In this work, we attempted to translate these results to persons with cervical SCI and investigated whether executed reach and attempted grasp actions could be decoded from their EEG signals. For this, we chose three different reach-and-grasp actions, two unimanual and one bimanual, towards objects of daily life. During participants’ self-initiated, executed reach and attempted grasp actions, we recorded EEG using mobile, water-based electrodes. We measured two participants with subacute cervical SCI who had preserved shoulder movements and elbow flexion but no wrist and hand functions. Both repeated the session three times. We also recorded the EEG of 10 non-disabled persons performing the same tasks (control group). We extracted and analyzed movement-related cortical potentials (MRCPs) from the EEG’s low-frequency time domain. Consecutively, we assessed the decoding capabilities of two linear (shrinkage based linear discriminant analysis (sLDA), linear support vector machine (SVM)) and two non-linear (Random Forests (RF), naive Bayes (NBC)) classification models for the discrimination of the grasp actions.We could show that sLDA, SVM, and Random Forest yielded comparable classification results on average with 63.4% ± SD 9% for participants with SCI and 69.7% ± SD 9% for the control group (chance level 29.3%). Our results indicate that it is feasible to decode executed reach and attempted grasp actions from MRCPs of persons with subacute cervical SCI. Future measurements will provide additional data to assess the generalizability of our results in a larger group of people with cervical SCI.
Belief Space Control with Intention Recognition for Human-Robot Cooperation

The cooperation between humans and robots is of great importance e.g. in medical, industrial or service applications. Here, the task to be pursued by the robot often depends on the current goal of the human. In cases where a direct communication of the human’s goal is impractical or even impossible, an estimation of the human’s goal is necessary. This estimation as well as potential process or measurement noise introduces uncertainty that needs to be taken into consideration during the planning of the robot’s actions. To this end, we propose an automation comprising an Unscented Kalman filter as state estimator, a model based intention recognition algorithm to estimate the human’s goal and a model predictive belief space controller based on Belief i-LQG explicitly considering the estimation uncertainty. We report on a simulated scenario featuring a mobile robot platform cooperating with a human. It demonstrates the ability of the proposed automation to actively reduce uncertainty about the system states and the human’s goal while successfully pursuing the overall cooperative task.
Stochastic Scenario Generation for Wind Power and Photovoltaic System Based on CGAN

With the high proportion accession of renewable energy, the uncertainty of the power system gradually increases. Scenario generation is an important method to describe the uncertainty of a high proportion of renewable power system, and plays an important role in the operation planning and scheduling of power systems. In this work, we proposed a wind power and photovoltaic random scenario generation method based on Conditional Generative Adversarial Networks (CGAN), Combined with the k-means clustering method to cluster and label the historical output power data, the mapping relationship between the noise input and the real output power data under the constraint of the clustering label is learned through the game mechanism of the generative adversarial network, different types of possible scenarios that retain features such as spatiotemporal correlations of real data are generated, the effectiveness and applicability of the proposed method are verified by experimental analysis of actual data. The simulation results show that CGAN can efficiently generate scenarios that conform to the characteristics of real data according to the conditions.
A thin film and high roughness flexible current collector for high charging/discharging rate flexible Li-ion battery

In this work, a 5.6 um thick, Rq 637.98 um roughness flexible Parylene C current collector is developed by using a rough Si wafer as a template. The rough current collector achieves 31% capacity retention @ 8C charging and discharging rate comparing the capacity @ 0.2C, which is 2.1 times higher than the sample using a flat current collector.
Robotic grasping target detection based on domain randomization

In recent years, deep learning has been a great success in robotic vision grasping, which is largely due to its adaptive learning capability and large-scale training samples. However, the hand-crafted datasets may suffer the dilemma of time-cost and quality. In this paper, a robot grasping target detection algorithm based on synthetic data is proposed. The training samples are generated quickly and accurately by domain randomization technique. Each RGB image of the domain randomized dataset contains complex backgrounds and randomly rotated detection targets, while the illumination of the scene and the occlusion of the targets are randomized to improve the generalization of the model, and finally we put the dataset into YOLOv3 for training. The YCB dataset is used as the training and testing samples. The experiments compare the detecting effects of the networks that are trained by YCB dataset and its synthetic data respectively. The results show that the dataset by domain randomization is consistent with the YCB dataset in terms of recognition accuracy, while the mAP of the dataset by domain randomization is improved by 10% compared to the YCB dataset, which further indicates that the synthetic dataset constructed by domain randomization can effectively improve the network learning effect and further improve the recognized performance of the target in complex scene.
3D Object Detection Based on Multi-scale Feature Fusion and Contrastive Learning

3D object detection plays an increasingly important role in the understanding of real natural scenes. In recent years, the method based on Hough voting has attracted more and more attention because of its compact model and high efficiency. However, the current voting strategy only uses poor local proposal information, which is not conducive to model optimization and performance improvement. A 3D object detection model based on multi-scale feature fusion and contrastive learning is proposed in this paper. The proposal stage focuses on voting to generate proposals, including the coordinates of proposals and the corresponding feature vectors. In order to obtain the local structure information, then calculate the multi-scale attention, and trace the multi-scale features of the proposal, an additional proposed coding contrast branch is introduced, which uses the proposed feature coding contrast loss to jointly optimize the feature representation and multi-scale attention modules. We have obtained competitive results on two large datasets SUN RGBD and ScanNet, which shows the effectiveness of our method.
Transient Analysis of Diffusion Recursive Least Squares via Signed Error Algorithm for Cyclostationary Colored Inputs

In this paper, we perform the transient theoretical analysis of diffusion recursive least squares via signed error (DRLS-SE) algorithm over networks in the presence of impulsive noise. The obtained analytical models allow us to investigate the impacts of nonstationary system and cyclostationary colored inputs on the network transient convergence behavior. Simulations are provided to highlight the robustness of DRLS-SE algorithm against impulsive noise, and corroborate the correctness and accuracy of obtained theoretical findings.
Task Offloading for Multi-Gateway-Assisted Mobile Edge Computing Based on Deep Reinforcement Learning

An effective task offloading strategy in the mobile edge computing enables terminals to migrate their tasks to the edge server, accelerating the execution of terminal tasks. However, most researches on task offloading are limited to the single edge server, while in practice, it is difficult for a single edge server to support joint offloading requests from multiple terminals. Edge gateways can be flexibly deployed around terminal devices to further reduce the computing load of edge servers. Therefore, we jointly study the task offloading problem in the multi-gateway-assisted mobile edge computing scenario. Constrained by discrete environmental variables, the offloading process jointly optimizes user scheduling, task offloading rate, and gateway resource allocation, with evaluation indexes defined by the average task delay and energy consumption. Aiming at minimizing the long-term cost of the whole system, we design a deep reinforcement learning algorithm with dynamically adjusted offloading strategies and allocated resources with only the partial state information. The simulation results demonstrate that the algorithm can obtain the optimal computation offloading policy in an uncontrollable dynamic environment. Compared with the other four benchmark algorithms, it has better system cost performance, and can quickly converge to the optimum. Meanwhile, In order to ensure the relative load balance on multiple gateways, we design a low-complexity balanced offloading strategy among multiple gateways and verify its performance.
Unsupervised Learning of Domain-Independent User Attributes

Learning user attributes is essential for providing users with a service. In particular, for e-commerce portals which deal in variety of goods ranging from clothes to foods to home electronics, it is especially important to learn “domain-independent” attributes such as age, gender, and personality that affect people’s behavior across various domains of daily life (e.g., clothing, eating and housing) because these attributes can be used for personalization in diverse domains their service covers. Thus far, researchers have proposed approaches to learn user representation (UR) from user-item interactions, trying to embed rich information about user attributes in UR. However, very few can learn URs that are domain-independent without confounding them with domain-specific attributes (e.g., food preferences). This could consequently undermine the former’s utility for personalizing services in other domains from which the URs are not learned. To address this, we propose an approach to learn URs that exclusively reflect domain-independent attributes. Our approach introduces a novel multi-layer RNN with two types of layers: Domain Specific Layers (DSLs) for modeling behavior in individual domains and a Domain Independent Layer (DIL) for modeling attributes that affect behavior across multiple domains. By exchanging hidden states between these layers, the RNNs implement the process of domain-independent attributes affecting domain-specific behavior and makes the DIL learn URs that capture domain-independence. Our evaluation results confirmed that the URs learned by our approach have greater utility in predicting behavior in the other domains from which these URs were not learned thereby demonstrating adaptability to various domains.
The Role of EEG-based Brain Computer Interface using Machine Learning Techniques: A Comparative Study

A brain-computer interface (BCI) is a way for humans and machines to communicate directly. It has been used in many successful ways over the last few decades. A wide variety of fields, from economics to biology, make heavy use of machine and deep learning techniques these days. In general, these strategies may be employed in two ways: adapting well-known models and architectures to the given data or creating custom architectures. There are a lot of ways to speed up the research process, so it is important to know which types of models work best for a certain problem and/or data set. For the first time, a machine learning benchmark for EEG signal classification is presented in this paper. This review article takes a close look at the Brain-Computer Interface (BCI) and how it makes use of Machine Learning (ML) technology. It examines the many sorts of research in this area and explores the significance of ML in executing various BCI tasks. These include mental state detection, task categorization, categorization of emotional states, classification of electroencephalogram signals and the occurrence of event-related potentials (ERP), as well as motor imagery classification. A comparative assessment of the studied approaches is provided in this study, which investigates feature extraction, selection, and classification. BCI and ML advancements are discussed in this article, as well as the types of improvements that will be necessary in the future to produce better BCI applications.
Ethernet Packet Transformation and Transmission Between Modbus/TCP and USB 3.0 with Field-Programmable Gate Array Development Board

This paper presents an Ethernet packet transformation and transmission architecture between Modbus transmission control protocol (Modbus/TCP) and universal serial bus (USB) 3.0 developed with a field-programmable gate array (FPGA) development board. The proposed architecture is used to complete packet transformation and transmission between Ethernet and USB 3.0 for application in plant automation. The Ethernet receiver receives and analyzes Modbus/TCP packets and sends the source address, destination address, IP header, and Modbus/TCP header to the register to verify the correctness of the packet. The Modbus/TCP packet is stored in static random access memory and awaits access by a USB 3.0 module. An FPGA development board (Intel DE10-Standard) is used for functional verification. The measured results show that the latency, throughput, and dynamic power are 18.845 ×s, 747.45 Mbps, and 142.17 mW, respectively, at a voltage of 1.8 V and operating frequency of 125 MHz.