Miracle-dz/UniFAR-FTUs huggingface dataset:
Microwave Link Failures Prediction via LSTM-based Feature Fusion Network

Microwave links are widely employed in cellular data networks due to high-speed Internet access and easy installation, thus reducing network implementation costs. However, these links are prone to failure and may lead to performance degradation, unavailability and service disruption. Early detection of any link failures is critical to maintain network quality, but the complex environment and the dynamic nature of link information makes this a complicated process. In this work, we propose a Long Short-Term Memory (LSTM)-based feature fusion network (LSTM-FFN) to fuse and encode both homophy and structural equivalence relationships in the LSTM temporal feature learning network. This will simultaneously model the spatial and temporal features exhibited in Long-Term Evolution (LTE) networks to detect any link failures. Our proposed method effectively avoids the gradient exploding problem that RNN-based STGNN faced. This multi-scale topological feature fusion allows the LSTM-FFN to further explore the spatial dependencies among nodel/ink and include additional structural equivalence in modeling compared with previous network failure detection work. The evaluation results show that LSTM- FFN outperforms other statistical-based methods with and without network topology encoded, and reaches 94.1 % precision, 90.2 % recall and 92.1 % fl-score.
Identifying Oil Spill Types Based on Remotely Sensed Reflectance Spectra and Multiple Machine Learning Algorithms

An accurate identification of oil spill types is the basis of determining the source of leakage, evaluating the potential damage, and deciding a plan of responses for an oil spill event. Despite sufficient studies that interpreted and analyzed hyperspectral data of oil spills, these studies that identify or classify oil spill types is rather limited. Aiming at identifying different types of oil spills, this article analyses the reflectance spectra obtained from high-resolution hyperspectral sensors using multiple machine learning methods. Four types of machine learning models are applied in this article: random forest; support vector machine (SVM); and deep neural network (DNN); and DNN with differential pooling (DP-DNN). The training and testing data are collected by field experiments under different environmental condition in order to verify the robustness of the machine learning models. The characteristics of reflectance is briefly described, and the results conform with results from previous studies. The performances of the machine learning models are evaluated and compared in terms of both accuracy of prediction and computational complexity. The results indicate that the two DNN models are able to achieve the most accurate prediction among the four machine learning models at the cost of more computation. The SVM model, or the proposed DP-DNN model may be a favorable choice when training time is limited.
Joint Beamforming for Integrated Mmwave Satellite-Terrestrial Self-Backhauled Networks

Satellite communication is considered as an efficient way to achieve the reliable communication for worldwide users. To further enhance the achievable rate of the satellite system, we propose joint beamforming schemes of an integrated satellite-terrestrial self-backhauled (ISTS) system, in which the satellite backhaul and terrestrial access links are operating at the same millimeter wave band. Specifically, the earth station and the base station (BS) constitute a self-backhauled relay to forward the satellite signals to the terrestrial users. A sum rate maximization problem is formulated under the constraints of the maximum power requirements of the satellite and the BS, the minimum quality-of-service requirements of the terrestrial users, and the maximum satellite backhaul capacity. To obtain some insights, we first assume that the perfect channel state information (CSI) on the satellite and terrestrial links is known and transform the formulated optimization problem to a standard difference of convex problem. Then we solve it iteratively by adopting the convex-concave procedure approach. Furthermore, when only imperfect CSI on the satellite and terrestrial links is available, we employ a iterative penalty function algorithm associated with the sequential convex approximation method and the S-procedure approach to obtain suboptimal beamforming vectors. Finally, simulation results demonstrate that the proposed joint beamforming schemes have better performance compared with other benchmark schemes and the ISTS architecture is suitable for the satellite systems in various orbits.
Anomaly Detection in Digital Twin Model

As the simulation model of a physical system, digital twin has been widely used in many complicated control systems. Providing an effective way to perform simulation, digital twin makes the evaluation, prediction and optimization process cheaper and easier than on physical systems. For smart manufacturing, digital twin achieves high productivity with less operation and maintenance cost. However, the advantages of digital twin in anomaly detection during manufacturing are always neglected. Taking a two-speed transmission system as an example, we generate three kinds of faults on this digital twin model. After extracting features from the system output, we train an anomaly detection model on these features to classify each type of fault. By this digital twin model, we can find the fault in the whole system at the very beginning, and reduce the time and cost of debugging and diagnosing.
Modeling, Design, and Evaluation of Active $\mathrm{\text{} d }v/\mathrm{\text{} d }t$ Balancing for Series-Connected SiC MOSFETs

Series connection of SiC <sc>MOSFET</sc>s provides an effective alternative to achieving higher blocking voltage with simpler circuit topologies. However, the voltage imbalance during the switching transient remains a critical issue. Recently, an active <inline-formula><tex-math notation="LaTeX">$\mathrm{\text{} d }v/\mathrm{\text{} d }t$</tex-math></inline-formula> control approach utilizing a controllable equivalent Miller capacitor has been proved to be an effective, low-loss, and compact solution. This article renders an improved control circuit with comprehensive modeling and analysis. First, the original circuit is modified with an additional bipolar-junction-transistor and pulsed control signal so that the external capacitor can be fully reset every switching cycle. Second, a simplified model of the active <inline-formula><tex-math notation="LaTeX">$\mathrm{\text{} d }v/\mathrm{\text{} d }t$</tex-math></inline-formula> control is derived to unveil the linear correlation between the control voltage and the device <inline-formula><tex-math notation="LaTeX">$\mathrm{\text{} d }v/\mathrm{\text{} d }t$</tex-math></inline-formula> during the turn-<sc>off</sc> transient. Third, a feedback control model is described by difference equations for stability analysis, offering parameter selection guidelines for the control process. Fourth, experimental results with two series-connected SiC <sc>MOSFET</sc>s under 1.5-kV dc-link voltage are demonstrated to validate the open-loop control model and closed-loop stability. Finally, the control method is expanded to eight series-connected devices under 6 kV to prove its scalability and potential for medium-voltage high-current applications.
Extended Attention Mechanism for TSP Problem

The attention mechanism has shown good performance in many aspects such as machine translation. But it is not completely suitable for combinatorial optimization problems. In the Traveling Salesman Problem(TSP), there is no need to consider the city nodes that have been traveled, but only the city ones that have not been traveled to in the future. To address this problem, this paper extends the attention mechanism to solve combinatorial optimization problems such as the traveling salesman problem. We train a Gated Recurrent Unit(GRU) to predict a distribution over different city permutations with the input of a set of city coordinates, and use negative tour length as the reward signal, and optimize the parameters of the GRU using a policy gradient method. The experiments demonstrate that the extended attention mechanism achieves more close to optimum solutions than the globe attention and local attentional mechanism on two-dimension planar space graphs with up to 100 nodes.
Accurate Device Temperature Forecasting using Recurrent Neural Network for Smartphone Thermal Management

Technological innovations such as 5G, multiplayer online gaming, 8k video recording and incorporation of more camera sensors in smartphones have resulted in heat generation causing poor performance and high power consumption. Given that acceptable range of the device when in contact with human skin is 40~45°C, the generated heat can be uncomfortable for users. In order to have a balance between high performance experience and thermal hazard in smartphones, effectual thermal management strategies are needed. To address this challenge, in this paper we propose Thermal Foresight Engine (TFE) model which predicts the device skin temperature in advance, for prediction windows of 30 seconds, 1 minutes & 3 minutes with accuracy of 97%, 94% and 86% respectively with an average inference time of 17 ms, providing thermal foresight to smartphone performance management & thermal mitigation systems in use cases such as 5G, video recording and games. It uses a Long Short-Term Memory (LSTM) [1] model, which has an RNN[1] based architecture. It takes input as current & past workload conditions in the form of a system-wide contextual information. Applications of this research will assist mitigation strategy used in smartphone proactively. This will effectively handle thermal anomalies in smartphones without heat up by improving thermal awareness, preventing performance impact due to thermal anomalies.
Graph-based KB and Text Fusion Interaction Network for Open Domain Question Answering

The incompleteness of the knowledge base (KB) limits the performance of open domain question answering (QA). Represent the incomplete KB with graph attention network (GAT) and complement the incomplete KB by extra text achieves great success to boost the QA system when the KB is incomplete. In this paper, we propose a Graph-based KB and Text Fusion Interaction Network (GTFIN) to improve the performance of the incomplete QA system by utilizing the KB and text information.In GTFIN, to reduce the influence of the query-unrelated noisy information of GAT on final answer prediction, we first design a global-normalization graph attention network (GGAT) by determining the query-related edge weights from the global perspective, and then a coarse-to-fine text reader (CFReader) is proposed to both exploit the relation information and obtain the entity mention representation in the text to enhance the incomplete KB. We further incorporate a bi-attention mechanism to enhance the interaction between question and entity representation which could find more query-related entities for final answer prediction. On the widely used KBQA benchmark WebQSP, our model achieves state-of-the-art performance.
On the Optimal Base-Station Height in mmWave Small-Cell Networks Considering Cylindrical Blockage Effects

Small-cell networks (SCNs), especially those operating in millimeter-wave bands, are sensitive to blockages. In this letter, we develop a three-dimensional (3D) SCN model considering blockages to investigate the impact of base-station (BS) height, BS density and blockage density on the downlink coverage probability. More specifically, we model the blockages as cylinders whose locations follow a Poisson point process and model the locations of BSs as a Poisson hole process. We assume that all the BSs are of the same height and the blockage height follows an exponential distribution. Based on the 3D SCN model, we derive the exact integral expression of coverage probability for general SCNs and the closed-form expression of coverage probability for ultra-dense SCNs. Our analytical results are verified to be reliable through simulations. The numerical results quantify the impact of the blockage density and the BS height on the coverage probability. For a small blockage density, elevated BSs always degrade the coverage probability, while the coverage probability first increases and then decreases with the BS height when the blockage density becomes sufficiently large.
Nonlinear Luenberger observer for systems with quantized and delayed measurements

A Luenberger-like observer for nonlinear systems with quantized measurements is proposed. The observer design is based on the solution of a certain partial differential equation that is solved numerically. Then, stability of this observer is proved even in presence of quantized measurements and delayed measurements. The results are illustrated by an example.
R2L: Routing With Reinforcement Learning

In a packet network, the routes taken by traffic can be determined according to predefined objectives. Assuming that the network conditions remain static and the defined objectives do not change, mathematical tools such as linear programming could be used to solve this routing problem. However, networks can be dynamic or the routing requirements may change. In that context, Reinforcement Learning (RL), which can learn to adapt in dynamic conditions and offers flexibility of behavior through the reward function, presents as a suitable tool to find good routing strategies. In this work, we train an RL agent, which we call R2L, to address the routing problem. The policy function used in R2L is a neural network and we use an evolution strategy algorithm to determine its weights and biases. We tested R2L in two different scenarios: static and dynamic networks conditions. In the first, we used a 16-node network and experimented with different reward functions, observing that R2L was able to adapt its routing behavior accordingly. Finally, in the second experiment, we used a 5-node network topology where a given link's transmission rate changed during the simulation. In this scenario, we observed that R2L was able to deliver a competitive performance, compared to heuristic benchmarks, with changing network conditions.
Stimulus Paradigm for an Asynchronous Concurrent SSVEP and EOG Based BCI Speller System

The brain computer interface (BCI) speller system can be classified into synchronous and asynchronous type. In synchronous type, one of the target characters is selected with specific interval periodically even if the user is making or not making attention on the target, whereas, in asynchronous case, the target character will not be selected until a confirmation signal is received from the user. In this proposed study a novel oddball stimulus paradigm is introduced on the hybrid steady state visual evoked potential (SSVEP) and electrooculogram (EOG) speller system to achieve an asynchronous control and high performance. The proposed system consists of forty characters grouped and indexed into five flickering unique frequency visual stimuli. Each visual stimulus is assigned with eight unique characters. The characters in each group are randomly highlighted by the oddball paradigm and the user performs blink eye movement in synchrony with the desired target character highlight. An asynchronous control is achieved by EOG signal and oddball paradigm because the target character will not be selected until the user performs blink eye movement. The stimulus paradigm helps the user to select the target group and desired target character, concurrently by SSVEP and EOG signal which increases the performance of the system. The proposed asynchronous speller system is tested and validated on ten subjects. The online classification accuracy and information transfer rate (ITR) of the proposed hybrid speller system are 99.38% and 116.58 bits/min respectively. Performance metrics of the proposed system are compared with the conventional speller systems and is found to be much superior to existing systems.
Unsupervised Discovery of Transitional Skills for Deep Reinforcement Learning

By maximizing an information theoretic objective, a few recent methods empower the agent to explore the environment and learn skills without extrinsic reward. However, when considering using multiple consecutive skills to complete a specific task, the transition from one to another cannot guarantee the success of the process due to the evident gap between skills. In this paper, we propose a novel unsupervised reinforcement learning approach to learn transitional skills in addition to pursuing diverse primitive skills. By introducing an extra latent variable for exploring the dependence between skills, our method discovers both primitive and transitional skills by optimizing a novel information theoretic objective. Considering various robotic tasks, our results demonstrate the effectiveness on learning both diverse primitive skills and transitional skills, and further exhibit the superiority of our method in smooth transition of skills over the baselines. Videos of transitional skills can be found on the project website: https://sites.google. com/view/udts-skill.
Discovery of the Nearly Zero Flux Between Two Parallel Conductors in Planar Transformers

The momentum to achieve high efficiency, high frequency, and high power density in power supplies limits the use of conventional wire-wound transformers, but widely employs planar transformers. Planar transformers intrinsically benefit from low profile, predictable parasitic components, ease of manufacture, and excellent repeatability of construction, which are generally applied to high frequency and high current applications, such as data centers and telecoms. Reducing the current density through parallel connections is becoming common practice in planar transformers. However, the current on every parallel conductor is usually unbalanced and hard to be predicted. Start from the motivation to predict parallel current distribution, a phenomenon is discovered in this article that the parallel current distribution is constant from medium frequency (MF) and follows the same pattern in higher frequency range. Besides, this article points out that the insulation thickness also affects the current distribution. Furthermore, the phenomenon that the magnetic flux in the space between two parallel conductors approaches zero when parallel currents are frequency independent is proved theoretically and demonstrated experimentally. Together with Ampere's circuital law, which links the current to the magnetic field, the current distribution can be derived. No complex mathematical calculation or simulation tool is required. Any applications using planar transformers with parallel conductors at MF or higher frequency can adopt this method to predict the parallel current distribution for design optimization.
Kinematics, Dynamics and Control Design for a 4-DOF Robotic Manipulator

This paper presents the problems of kinematics, dynamics, and design of the precise position controller for a 4-DOF robotic manipulator. First, the forward kinematic and the inverse kinematic are addressed based on the convention of Denavit - Hartenberg (DH) theory. Second, the dynamics are analyzed by the general Euler-Lagrange equation and the formula of the Coriolis/ Centrifuge matrix. Third, the two control laws of precisely the end-effector position proposed as PD - computed torque controller (PD-CTC) and sliding mode controller (SMC). Finally, a numerical simulation will be provided to verify the superiority of the proposed controllers for the robotic manipulator.
A self-selected congestion scheme based on CAMs maximum utility function

In Vehicular Ad hoc Networks (VANETs), the periodic transmission of CAMs on the channel, the load of the channel increases, resulting in channel jam, such as traffic accidents, road congestion, wrong traffic decision information, etc. In response to the above problems, a self-selected congestion scheme based on CAMs maximum utility function(MUSC) was proposed in this paper. The proposed solution serves as a method of broadcasting safety information and ensures that the message is reliably and timely transmitted to all vehicles in the network. the MUSC scheme avoids channel congestion and queue overflow. In order to estimate the control effect of the MUSC scheme, it was compared with other congestion control schemes.
Analyzing LDA and NMF Topic Models for Urdu Tweets via Automatic Labeling

The understanding and analyzing of available content on Social media Platforms such as Twitter and Facebook, through various topic modeling methods is not supervised. However, despite several existing conventional techniques, they have had limited success when applied directly for filtering and quick comprehension of short-text contents due to text sparseness and noise. Thus, it always has been challenging to discover reliable latent topics from online discussion texts that prevail with low words co-occurrence and availability of large size social media benchmark datasets, even for resource-rich languages. The existing literature lacks such work for Urdu text to unveil niche topics even with conventional topic models, mainly due to the lack of benchmark datasets, limited availability of pre-processing tools/ algorithms, and time and compute limitations on large-sized datasets. This work presents experiments with multiple approaches of topic modeling like Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF) on 0.8 million Urdu tweets. These tweets are collected through Twitter API by giving various hashtags as a query to avoid dominance of single topic in the dataset. In addition, we have pre-processed the text of the tweets, prepared the three variants of the collected dataset, and extracted multiple features to represent documents on different n-grams. Furthermore, all these techniques are compared and evaluated on the dataset variants, using both qualitative and quantitative measures. We have also demonstrated the results of these approaches through visualization methods, graphs depicting tweets size per topic, word clouds, and hashtags analysis, giving insights about algorithms performances on finalized topics. Observed results reveal that NMF outperformed the techniques with TF-IDF feature vectors in Urdu tweets text, while LDA performed best with merging short-text strategy into long pseudo documents.