Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact Low-Loss Half-Mode Substrate Integrated Waveguide Filter With Controllable Transmission Zeros

Two novel planar filters with controllable mixed electromagnetic couplings are developed in this brief for C band operation. They are implemented with the half mode substrate integrated waveguide (HMSIW) technology. Owing to the half mode SIW cavity, both of the filters exhibit a compact size. The size of filter I is <inline-formula> <tex-math notation="LaTeX">$0.53\,\,{\boldsymbol{\lambda }}_{\mathbf {0}} \times 0.30 {\boldsymbol{\lambda }}_{\mathbf {0}}$ </tex-math></inline-formula>, while the size of filter II is <inline-formula> <tex-math notation="LaTeX">$0.47\,\,{\boldsymbol{\lambda }}_{\mathbf {0}} \times 0.28 {\boldsymbol{\lambda }}_{\mathbf {0}}$ </tex-math></inline-formula>. (<inline-formula> <tex-math notation="LaTeX">${\boldsymbol{\lambda }}_{\mathbf {0}}$ </tex-math></inline-formula> represents the wavelength in the free space at the center frequency). A wide passband response with a quasi-elliptic characteristic and good out-of-band rejection is realized due to the transmission zeros (TZs) introduced by the etched slot structures on the top metal layer. By modifying the dimensions of the structure, the strength of the electric and magnetic coupling can be independently controlled, and consequently the position of TZs is controllable to achieve desired stopband response with high selectivity. As a validation of the concept, one prototype is fabricated and tested. Excellent agreement is observed between the simulated and measured results.
The ZEKE-effect in cold Rydberg gases

Cold, dense Rydberg gases produced in a cold-atom trap are investigated using spectroscopic methods and time-resolved electron counting. On the discrete Rydberg resonances we observe large trap losses and long lasting electron emission from the Rydberg gas (>30ms). Our observations are explained by quasi-elastic l-mixing collisions between Rydberg atoms and slow electrons that lead to the population of long-lived high-angular-momentum Rydberg states. These atoms thermally ionize slowly and with large probabilities, leading to the observed effects.
Deep Reinforcement Learning toward Robust Multi-echelon Supply Chain Inventory Optimization

Multi-echelon supply chains (SC) are highly complex systems with many inherent uncertainties, including customer demands and transportation time from one location to another. In addition to the complex SC structures, inventory decisions made at different stages affect each other. Maintaining global-level optimal inventory levels along the entire supply chain that is robust to changing business needs remains challenging. In this work, we have developed a simulation environment, GymSC, for reinforcement learning of multi-echelon SC inventory policies. Using a series of model-free deep reinforcement learning algorithms, we trained dynamic SC policies that have significantly improved performance when compared with popularly used heuristics. The robustness of the learned policy is demonstrated by its adaptability to a previously unseen environment with non-stationary customer demands. The presented work showcases the effectiveness and robustness of deep reinforcement learning in solving important practical SC optimization problems. In addition, we present this configurable simulation environment as a platform for testing existing and new algorithms to develop robust inventory policies with a goal to encourage further integration of deep reinforcement learning into the SC management problems as a promising alternative for addressing inventory optimization with uncertainties.
Imbalanced Wafer Map Dataset Classification with Semi-Supervised Learning Method and Optimized Loss Function

Wafer is the crucial raw material of semiconductor devices. In wafer production, impurities cannot be removed entirely, which will cause various wafer map defects. Quickly and precisely classifying wafer map defects can help engineers track failures in the semiconductor manufacturing process. However, different wafer map defect patterns occur randomly and irregularly, and labeling work is labor-intensive and time-consuming. Therefore, the wafer map dataset is usually imbalanced and consists of many unlabeled data. In this paper, we utilize unlabeled data by using semi-supervised learning methods and alleviate the imbalance problem by optimizing the loss function to increase the accuracy of wafer map classification. The performance of the proposed method is illustrated with the WM-811K dataset which consists of real-world wafer maps.
Proximal Policy Optimization Based Reinforcement Learning for Joint Bidding in Energy and Frequency Regulation Markets

Driven by the global decarbonization effort, the rapid integration of renewable energy into the conventional electricity grid presents new challenges and opportunities for the battery energy storage system (BESS) participating in the energy market. Energy arbitrage can be a significant source of revenue for the BESS due to the increasing price volatility in the spot market caused by the mismatch between renewable generation and electricity demand. In addition, the Frequency Control Ancillary Services (FCAS) markets established to stabilize the grid can offer higher returns for the BESS due to their capability to respond within milliseconds. Therefore, it is crucial for the BESS to carefully decide how much capacity to assign to each market to maximize the total profit under uncertain market conditions. This paper formulates the bidding problem of the BESS as a Markov Decision Process, which enables the BESS to participate in both the spot market and the FCAS market to maximize profit. Then, Proximal Policy Optimization, a model-free deep reinforcement learning algorithm, is employed to learn the optimal bidding strategy from the dynamic environment of the energy market under a continuous bidding scale. The proposed model is trained and validated using real-world historical data of the Australian National Electricity Market. The results demonstrate that our developed joint bidding strategy in both markets is significantly profitable compared to individual markets.
Keypoint Localization Based on Convolutional Neural Network for Robotic Implantation of Flexible Micro-Electrodes

Visual localization of micro flexible electrode and implant needle is an important task for robotic flexible electrode implantation. Magnification switch, occlusion, defocus, illumination changes in microscopic imaging produce challenges for this task. We propose the Keypoint Localization and Angle Estimation Network (KLAE-Net) based on convolutional neural networks. KLAE-Net has two branches: the keypoint localization branch for obtaining the coordinates of electrode and needle in image space; the angle estimation branch for monitoring the inclination of needle. Attention mechanism and deformable convolution are used to improve the model’s performance. For training and evaluation under the flexible electrode implantation task, we construct a novel dataset containing 1000 images covering various conditions. An image Jacobian matrix based alignment control method is designed, to realize the robotic alignment between needle and electrode. A series of experiments are conducted with the dataset and an implantation robot system.
Robust Position Regulation of a Seesaw Actuated by a Humanoid

This paper presents the implementation of a robust control technique applied within a standard multi-objective robot controller for humanoid robots. The challenge is to regulate the angular position of a seesaw actuated by a humanoid on it, which is a complex multitask involving the humanoid balance, the motion to project its weight into its feet to move the seesaw, and the seesaw motion itself. An accurate model of the seesaw is not available, therefore, a model-free approach based on super twisting sliding mode control is proposed; besides, the stability of the closed-loop system with the proposed technique is proved using the Lyapunov’s direct method. The proposed controller was applied to a multi-robot model compound by the HRP-4 humanoid and a passive seesaw model, and it was tested using a physics-engine simulation environment. The presented results show that the challenge of regulating the seesaw was successfully achieved by means of the proposed robust control.
A Peer-to-Peer Market-Based Control Strategy for a Smart Residential Community with Behind-the-Meter Distributed Energy Resources

This paper presents a distributed peer-to-peer market control strategy to manage and to enable resource sharing of behind-the-meter distributed energy resources in a residential community. In the proposed strategy, each consumer or prosumer determines the flexibility of their point of connection to the power network such that the obtained flexibility is network-feasible. Based on the feasible flexibility, the consumers and the prosumers trade power among each other at each time instance to fulfil their preferred load requirements while maximizing their payoffs and helping to regulate node voltages inside the community. Because the problem to be solved is non-convex, a distributed particle swarm optimization algorithm is used to coordinate the consumers/prosumers in a fully autonomous manner without any centralized or hierarchical coordination. Numerical simulations performed on a community of 48 homes demonstrate the efficacy of the proposed approach.
Blood Potassium Concentration Estimation on Electrocardiogram Signals Using Machine Learning Techniques

Hyperkalemia is common for patients with chronic kidney disease (CKD) and it is often the cause of sudden cardiac death when it is not monitored. The detection of dyskalemias currently depends on laboratory tests. Reliable and rapid detection is therefore necessary. The electrocardiogram (ECG) can meet this need and improve the automatic detection of different levels of hyperkalemia before laboratory results. In this paper, we proposed a method that could be used to estimate blood kalemia levels based on a representation of ECG signals as images using two approaches: Dynamic Time Warping (DTW) and the time elapsed between two successive R-waves of the QRS signal on the electrocardiogram. Both images are fed to a convolutional neural network (CNN) that improved the monitoring routine without the need for invasive blood testing.
Multi-level Multi-channel Bio-signal Analysis for Health Telemonitoring

Technology advancements have enabled a wealth of health information to be remotely collected, resulting in increasing use of telemonitoring for patients with chronic diseases. In particular, multi-channel bio-signals such as ECG and EEG, gold-standard diagnostic approaches for many diseases, are able to be collected at home. To utilize multi-channel bio-signals for telemonitoring and telemedicine, it is critical to develop a rigorous prediction model that "translates" the monitored symptomatic signals into a clinical indicator of disease severity to facilitate disease monitoring and diagnosis. However, multi-level and multi-channel data pose major challenges for most statistical prediction methods. To address these challenges, this paper proposes a multi-level multi-channel framework to integrate multi-channel epoch-level bio-signals and other patient-level health covariates for precise prediction of disease severity in health telemonitoring. The proposed framework was applied in a real-world dataset consisting of 409 patients and achieved the highest prediction accuracy in comparison with benchmark methods that do not take account of the multi-level structure of features into prediction. The findings of this study demonstrate the efficacy and performance of the proposed method in predicting disease severity from multi-level multi-channel features, which contributes a novel predictive analytical tool to facilitate bio-signal-based health telemonitoring.
On the Way from Lightweight to Powerful Intelligence: A Heterogeneous Multi-Robot Social System with IoT Devices

As robots play an increasingly important role in people’s lives, researchers are working on robotic vehicles with powerful intelligence. However, a problem that cannot be ignored is resource constraints on the edge. Considering the gaming issues of resource constraints and intelligence level, we focus on robots with limited computing resources and propose an idea of changing from lightweight to powerful intelligence for a smart robotic system. Firstly, we design a series of ultra-lightweight algorithms according to the lightweight resource Limitation. Second, we collaborate the ultra-lightweight algorithms through a centralized-distributed architecture to achieve intelligent upgrading of the whole system. Then, by maximizing the use of resources and information, we accomplish a heterogeneous ultra-lightweight multi-robotic collaborative system. Finally, the presented architecture has been applied to realize a lightweight simultaneous localization and mapping (SLAM) system. Experimentally, the ultra-lightweight algorithm achieves 900 fps on the server experimental platform. Since there have been less heterogeneous collaborative methods, we further compared it with the state-of-the-art homogeneous collaborative system and proved that the accuracy of our proposed system was improved by 45.98%.
Chaotic few-body vortex dynamics in rotating Bose-Einstein condensates

We investigate a small vortex-lattice system of four co-rotating vortices in an atomic Bose--Einstein condensate and find that the vortex dynamics display chaotic behaviour after a system quench introduced by reversing the direction of circulation of a single vortex through a phase-imprinting process. By tracking the vortex trajectories and Lyapunov exponent, we show the onset of chaotic dynamics is not immediate, but occurs at later times and is accelerated by the close-approach and separation of all vortices in a scattering event. The techniques we develop could potentially be applied to create locally induced chaotic dynamics in larger lattice systems as a stepping stone to study the role of chaotic events in turbulent vortex dynamics.
A SDN Controller Monitoring Architecture for 5G Backhaul Networks

Carriers deployed 5G backhaul network towards multi-protocol label switching network with fixed-mobile convergence. With the demands and development of new 5G application, 5G backhaul, which can be regarded as a Wide Area Network (WAN), requires to meet more bandwidth, low latency and scalability. Traditional wide area networks have the issues of high cost, low efficiency, and global optimization for multidomain. The software-defined technology is introduced to the WAN, which is called WAN SDN controller, to satisfy the real-time and flexible requirements of 5G new service scenarios. To achieve next generation mobile network, telemetry and network observability is also important. In this paper, we propose a distributed architecture, applied to the real 5G networks, and analyze the amount of captured traffic data and delivery paths. Our solution deployed on 5G network in Chunghwa Telecom. The implemented outcomes show that the IP/MPLS networks automatically adjust to achieve closed-loop through WAN SDN controller. It lays the foundation for the goal of WAN autonomous networking.
Data-Driven Model Reduction of the Moving Boundary Heat Pump Dynamic Model

Heat pump systems have the potential to be used as controllable load to compensate for the uncertainties in modern power systems. The moving boundary model for heat pumps is known for its accuracy and acceptable computational burden. However, this model is sometimes impractical because it requires detailed information on the mechanical structure of the internal loops and the thermal state of the refrigerant. A data-driven method based on the cascaded wiener model is proposed in this paper to simplify the moving boundary model. The proposed model is developed from an observation that the dynamic behavior of the heat pump is relatively linear over a large range of operation states, while the static behavior is very nonlinear. Through comparison of simulation results, the proposed model has close accuracy to the moving boundary model and can be a viable alternative for control design purposes.
Time Based Concave Cache Pricing for Information-centric Networks

To distribute contents through Information-centric networks (ICN) was proposed as an important future Internet architecture, in which contents was considered as the main part. In ICN, contents name are the routing locator for efficient contents distribution. Contents are dynamically cached in network routers of Internet service providers (ISPs), which is different from traditional content distribution networks that still use IP address as routing locator. Caching has been identified as a critical component. Many cache mechanisms determine contents' cache time by the popularity of the contents, i.e., popular contents can be kept in cache for much longer time. Instead, our previous work has proposed a time-to-live based caching mechanism for ISPs to monetize their cache resource, in which the contents' cache time is determined by contents provider's (CP's) payment, and the CP was charged by an affine price function of contents cache time in ISPs' cache. In this paper, a concave pricing mechanism is considered, and CP is charged by an concave price function of contents cache time in ISPs' cache. We theoretically analyze the proposed concave pricing mechanism and provide solution for CP to optimally choose the time length stay in ISP's cache.
Personality Predictive Analysis Based on Artificial Neural Network

Individual personality is a vital criterion in a human life as well as in a development of an organization. Every individual holds a different personality type with various characteristics in terms of thinking, feeling, and behaving. Due to that matter, analyzing human personality should be done carefully which can be challenging and time consuming. Therefore, this study presents a personality prediction model based on Artificial Neural Network (ANN). The prediction was realized on the Big Five personality model. The dataset consists of 709 rows and retrieved from open-source website called Kaggle. For training, validation and testing phases, the dataset was divided into 70:15:15 respectively. Findings of the study demonstrated the capability of ANN in producing better accuracy compared to three identified algorithms which includes Naïve Bayes, ZeroR and Random Forest.
Radar-Based Gesture Recognition with Spiking Neural Networks

Spiking neural networks (SNN) are a promising approach for low-power edge AI (artificial intelligence), especially when run on dedicated neuromorphic hardware. In this work we set up a SNN in TensorFlow, directly train it in a supervised manner with backpropagation through time (BPTT) and surrogate gradients, and compare it with traditional neural networks, based on convolutional neural networks (CNN) and long-short term memory (LSTM) cells, for radar-based hand-gesture recognition. We demonstrate that a small SNN with only 30 hidden leaky integrate-and-fire (LIF) neurons and threshold encoding can achieve an accuracy of 98.1%. With the more complex adaptive LIF neuron, the activity can be reduced by up to 37.9% without significant loss in accuracy. A comparison to traditional LSTM-networks shows the superiority of the SNN in terms of accuracy and computation cost, indicating that they are a considerable alternative to LSTM-based approaches.
A Blockchain Based Co-simulation Framework for Integrating DERs to Wholesale Electricity Markets

As the number of distributed energy resources (DERs) continue to increase across energy-delivery systems, there remains a need for integrating their capabilities into traditional grid operations. In this paper a blockchain-based solution is proposed to facilitate FERC's Order No. 2222 implementations. The presented use-case enables small-scale DERs to participate in wholesale market operations through DER aggregators, while also enabling local distribution system operators to enforce distribution system constraints in a secure and traceable manner. The presented use case is built around the Unified Testing Platform (UTP) being developed as a part of the Blockchain for Optimized Security and Energy Management (BLOSEM) project. This is a multi-lab effort intended to simplify the deployment of blockchain-powered grid solutions by enabling the integration of simulation tools, and blockchain technologies through the use of system-agnostic interfaces that provide a modular, interoperable, and reusable connectivity layer.
HVDC Line Fault Location Using Wavelets to Mitigate Impact of Frequency Dependent Line Parameters

Accurate fault location method is beneficial since it reduces the power outage time and operational costs. This paper proposes a time domain Bergeron model based method to accurately locate faults in HVDC transmission lines. The line mode and high frequency component of measurements are utilized. In addition, the method utilizes wavelets to choose the proper line parameters used in the fault location algorithm. Compared to the existing method, the proposed method greatly mitigates the impact of frequency dependent line parameters. Numerical experiments in an example 500 km VSC-HVDC line demonstrate that the proposed fault location method has higher fault location accuracy than the existing method, regardless of fault types, locations and impedances. Moreover, the method only requires a short fault data window of 5 ms and a relatively low sampling rate of 20k samples/sec.
Toward Characterization of the Feasible Region of Unbalanced Distribution Systems

The feasible region of an unbalanced distribution system (FR-UD) plays an important role in operation and optimization of an unbalanced distribution network (UD). This paper develops a complete characterization of the FR-UD taking into account the three-phase AC power flow modeling, distributed energy resources (DERs) and flexible loads (FLs), voltage constraints and thermal limit constraints. Theoretically speaking, this paper derives a complete characterization of FR-UD. In addition, a QGS-based numerical method is proposed as a feasible solution for the entire FR-UD and is numerically demonstrated on UD test systems. Numerically speaking, cases are used to evaluate the precise influence of the thermal limit constraint on FR-UD. Moreover, the effect of FLs on DER curtailment mitigation and improving system flexibility is examined.