Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-period planning of distribution grid reinforcements under uncertainty about future penetration of photovoltaic systems

The policy transition towards renewable sources of energy generation has triggered a proliferation of small, independent, and decentralized power plants such as solar PV panels owned by households. This presents network operators with challenges regarding the maintenance and expansion of low-voltage distribution grids. We model multi-period planning of grid reinforcement on the basis of three investment strategies: a purely reactive strategy that disregards any information about the future, a proactive strategy that requires only a probability distribution of future distributed generation, and a hypothetical strategy assuming perfect information. Simulations show that 75% of the time the proactive strategy achieves equal or lower total reinforcement cost than the reactive strategy, which can be regarded as the industry standard. On average, it yields about 30% of theoretically possible savings. The results are robust to changing simulation parameters, including PV penetration levels.
Efficient implementation of convolutional neural networks on FPGA

Cyber-Physical Systems (CPS) apply complex machine learning algorithms to process incoming sensor signals to interact with the embedding environment through actuators, which is an inherently real-time task. Therefore, machine learning algorithms must be also evaluated real-time, bringing up very delicate requirements about implementations. Convolutional Neural Networks (CNNs) are artificial neural networks successfully applied in machine vision applications, therefore, their use in CPS is primarily determined by the efficiency of the implementation. CNNs are typically implemented using general purpose CPUs, DSPs, and GPGPUs, however, due to their structure and operation, they are also very good candidates to FPGA implementations. The paper will present an efficient implementation of CNNs on FPGAs and an initial evaluation of the implementation using the LeNet network.
Study of text emotion analysis based on deep learning

In traditional sentiment classification methods, sentiment-based methods rely heavily on the quality and coverage of sentiment lexicons, whereas machine-based approaches rely on features of manual construction and decimation. In recent years, deep learning technology has made great progress in the field of natural language processing, depth model has more powerful skills. This article focuses on several commonly used deep learning models for textual affective classification and compares their strengths and weaknesses.
Linear accuracy of cone-beam computed tomography and a 3-dimensional facial scanning system: An anthropomorphic phantom study

Purpose This study was conducted to evaluate the accuracy of linear measurements of 3-dimensional (3D) images generated by cone-beam computed tomography (CBCT) and facial scanning systems, and to assess the effect of scanning parameters, such as CBCT exposure settings, on image quality. Materials and Methods CBCT and facial scanning images of an anthropomorphic phantom showing 13 soft-tissue anatomical landmarks were used in the study. The distances between the anatomical landmarks on the phantom were measured to obtain a reference for evaluating the accuracy of the 3D facial soft-tissue images. The distances between the 3D image landmarks were measured using a 3D distance measurement tool. The effect of scanning parameters on CBCT image quality was evaluated by visually comparing images acquired under different exposure conditions, but at a constant threshold. Results Comparison of the repeated direct phantom and image-based measurements revealed good reproducibility. There were no significant differences between the direct phantom and image-based measurements of the CBCT surface volume-rendered images. Five of the 15 measurements of the 3D facial scans were found to be significantly different from their corresponding direct phantom measurements (P<.05). The quality of the CBCT surface volume-rendered images acquired at a constant threshold varied across different exposure conditions. Conclusion These results proved that existing 3D imaging techniques were satisfactorily accurate for clinical applications, and that optimizing the variables that affected image quality, such as the exposure parameters, was critical for image acquisition.
On registration of vector maps with known correspondences

Data association and registration is an important and actively researched topic in robotics. This paper deals with registration of two sets of line segments, which is especially useful in mapping applications. Our method is non-iterative, finding an optimal transformation in a single step, in a time proportional only to a number of the corresponding line segments. The procedure also provides diagnostic measures of reliability of the computation and of similarity of the data sets being registered. At this point, the method presumes known correspondences, which is limiting, but the discussion in the end reveals some possibilities to overcome this issue, which we are examining. Practical properties are demonstrated on a typical task of localization of a robot with a known map.
Action Recognition From Arbitrary Views Using Transferable Dictionary Learning

Human action recognition is crucial to many practical applications, ranging from human-computer interaction to video surveillance. Most approaches either recognize the human action from a fixed view or require the knowledge of view angle, which is usually not available in practical applications. In this paper, we propose a novel end-to-end framework to jointly learn a view-invariance transfer dictionary and a view-invariant classifier. The result of the process is a dictionary that can project real-world 2D video into a view-invariant sparse representation, and a classifier to recognize actions with an arbitrary view. The main feature of our algorithm is the use of synthetic data to extract view-invariance between 3D and 2D videos during the pre-training phase. This guarantees the availability of training data, and removes the hassle of obtaining real-world videos in specific viewing angles. Additionally, for better describing the actions in 3D videos, we introduce a new feature set called the 3D dense trajectories to effectively encode extracted trajectory information on 3D videos. Experimental results on the IXMAS, N-UCLA, i3DPost and UWA3DII data sets show improvements over existing algorithms.
ThunderLoc: Smartphone-Based Crowdsensing for Thunder Localization

Thunder localization provides an important solution to lightning location systems. This paper designs a smartphone- based thunder localization system, ThunderLoc. The key idea is to turn the localization problem into search problem in Hamming space by collecting the dual-microphone data of smartphones via crowdsensing mechanism. We utilized the TDOA of dual- microphone integrated in smartphone. After the quantization with a bit for the TDOA measurement from the smartphone nodes, thunder localization is performed by minimizing the Hamming distance between the measured binary sequence and the binary vectors in a database. Evaluation results demonstrate that ThunderLoc can effectively localize the virtual thunder with good robustness.
Handling large-scale action space in deep Q network

Deep reinforcement learning (DRL) is a new topic in recent years. Deep Q Network is a popular DRL implement. It is a well-studied technique and has achieved significant improvement on several challenging tasks such as Atari 2600 games. However, in some kinds of games, there are a large number of possible actions. Thus the output layer in DQN could be complicated due to the large-scale action space, which could harm the performance of DQN. In this paper, we proposed a variant structure of DQN to handle this problem. We could reduce the size of output layer in DQN. The experimental results show that our method improves significantly in some tasks with large-scale action space.
Comments on “Upper and Lower Bounds for Controllable Subspaces of Networks of Diffusively Coupled Agents”

A condition for controllability of a network of diffusively coupled linear agents with sparse actuation, which was developed in [1], is examined. An example is presented which demonstrates that the condition is not always sufficient for controllability. The gap in the original proof is clarified, and issues related to developing a necessary and sufficient condition are briefly discussed.
A Flexible and Energy-Efficient Convolutional Neural Network Acceleration With Dedicated ISA and Accelerator

State-of-the-art convolutional neural networks (CNNs) usually have a large number of layers and filter weights which bring huge computation and communication overheads. A general purpose instruction set architecture (ISA) is flexible but has low code density and high power consumption. The existing CNN-specific accelerators are much more efficient but usually are inflexible or require a complex controller to handle the computation and data transfer of different CNNs. In this brief, we propose a new CNN-specific ISA which embeds the parallel computation and data reuse parameters in the instructions. An instruction generator deploys the instruction parameters according to the feature of CNNs and hardware’s computation and storage resources. In addition, a reconfigurable accelerator with 225 multipliers and 24 adder trees is realized to obtain efficient parallel computation and data transfer. Compared with x86 processors, our design has 392 times better energy efficiency and 16 times higher code density. Compared with other state-of-the-art accelerators, our solution has a higher flexibility to support all popular CNNs and a higher energy efficiency.
Comparing neural architectures for demand response through model-free reinforcement learning for heat pump control

As batch reinforcement learning algorithms reach maturity and neural networks are used increasingly in reinforcement learning, a performance comparison of these models should be performed. This paper discusses the implementation of a heat pump agent in a demand response setting and its cost effectiveness when implemented with different neural network types. The agent maintains the interior air temperature of a building between pre-set temperature constraints, with four actions at its disposal. The agent is incentivized to shift loads in a day-ahead market in order to minimize daily electricity costs. The simulation considered a multilayer perceptron (MLP), a convolutional neural network (CNN) and a long short-term memory neural network (LSTM) to model the environment dynamics. All architectures outperform a trivial thermostat controller and shift loads successfully after 20–25 days. For this particular setup, there is no significant difference between the MLP and the LSTM, while they do outperform the CNN model. The MLP is preferred as it requires far less computation time.
LoRaWAN communication protocol: The new era of IoT

The LoRaWAN communication protocol is more and more used in the Internet of Things (IoT) concept. Many research centers have addressed the problem of wireless sensor networks, thus making a sustained effort in increasing the performance of WSN networks. The IoT concept promises to revolutionize and change the way we do our daily business and allow us to overcome current challenges such as the energy crisis, resource depletion and environmental pollution. The potential of the IoT concept is immense and comes to solve the major challenges of the M2M (Machine-to-Machine) industry. LPWA (Low-Power Wide-Area) wireless networks come to complete the traditional wireless and short-range wireless communications technologies in order to fulfill the various requirements of IoT applications. LoRa modulation is using a chirp (Compressed High Intensity Radar Pulse) spread spectrum (CSS) technique which offer resilience to interference, great communication link budget, performance at low power while being resistant to multi-path propagation fading and Doppler effect. The main contribution of this paper is the performance evaluation and sustainability analysis of LoRa technology.
Joint sparsity based heterogeneous data-level fusion for multi-target discovery

Most traditional surveillance systems use decision-or feature-level fusion approaches to integrate heterogeneous sensor data, which are sub-optimal with information loss. In this paper, we continue our previous work on joint-sparse data-level fusion (JSDLF) to integrate heterogeneous sensor data. Since heterogeneous sensor data originate from the same targets of interest, whose states can be determined by only a few parameters, it is reasonable to assume that the sensor measurement domain has a low intrinsic dimensionality. The proposed JSDLF approach is based on the joint sparse signal recovery techniques by discretizing the target state space. The JSDLF approach can fuse signals from multiple distributed passive radio frequency (RF) sensors and video sensor data for joint target detection, state estimation, and high level information fusion situation awareness. The JSDLF approach is data-driven and requires minimum prior information, since there is no need to know the time-varying RF signal amplitudes, or the image intensity of the targets. It can handle non-linearity in the sensor data due to state space discretization and the use of frequency/pixel selection matrices to reduce user involvement. In our previous work, the JSDLF approach was developed for a single-target case. In this paper, we extend the JSDLF approach to detect and estimate multiple targets. For a multi-target case with J targets, the JSDLF approach only requires discretization in a single-target state space, instead of discretization in a J-target state space, as in the case of the generalized likelihood ratio test (GLRT) or the maximum likelihood estimator (MLE). Numerical examples are provided to demonstrate that the proposed JSDLF approach achieves excellent performance with accurate target position and velocity estimates to support situation awareness.
Determination of nanocapillaries dimensions in insulative oil impregnated electrotechnical pressboard

The paper presents a stand for measuring the oil absorption rate during impregnation of electrotechnical pressboard or other porous materials. The stand allows for accurate record of absorbed oil volume and the time in which it was absorbed. It was found that the time needed to complete impregnating of pressboard is about 2×106 s. At this time, pressboard absorbs the insulating oil in a volume of approx. 25.6% its volume. This means that the free spaces occupy about 25.6% of pressboard volume. The testing of the pressboard free surface showed that it is about 10 m2 per cm3. Based on these data, it was found that the average value of capillary diameter in the pressboard is about 25.6 nm.
Passenger Demand Prediction with Cellular Footprints

Accurate forecast of citywide passenger demand helps online car-hailing service providers to better schedule driver supplies. Previous research either uses only passenger order history and fails to capture the deep dependency of passenger demand, or is restricted on grid region partition that loses physical context. Recent advance in mobile traffic analysis has fostered understanding of city functions. In this paper, we propose FlowFlexDP, a demand prediction model that integrates regional crowd flow and applies to flexible region partition. Analysis on a cellular dataset covering 1.5 million users in a major city in China reveals strong correlation between passenger demand and crowd flow. FlowFlexDP extracts both order history and crowd flow from cellular data, and adopts Graph Convolutional Neural Network to adapt prediction for regions of arbitrary shapes and sizes in a city. Evaluation on a large scale data set of DiDi Chuxing from cellular data shows that FlowFlexDP accurately predicts passenger demand and outperforms the state-of-the-art demand prediction methods.
A transfomer-based converter with a negative feedback regulation for energy harvesting

An improved transformer-based converter with a negative feedback regulation is proposed in this paper. Compared with the conventional designs which only have a positive feedback loop for oscillation, the proposed design adopts both positive feedback and a negative feedback loop which avoids the extra energy from the load dissipation releasing to the ground and well regulates the output voltage. A Schottky diode branch is used to reduce the spike energy induced by the oscillation and accelerates the start-up process. The proposed converter is well implemented in a 0.4 μm CMOS process. As the simulation results show, with an input voltage ranging from 49 to 400 mV, the maximum output power of 3 mW and the peaking transformer conversion efficiency of 48% are obtained at the regulated voltage of 3 V in this study.
PV inverter topology with high efficiency and reduced ground leakage current

Driven by worldwide demand for renewable sources, there is a strong trend in photovoltaic scenario to use transformer less inverter topologies to obtain higher efficiencies and lower ground leakage current. This paper presents a new topology based on H-bridge with ac decoupling scheme consisting of Diode Bridge and a switch with clamping to the DC midpoint. The PV panel is modeled to obtain a voltage of 350V. Also the topology is simulated and compared with two other existing topologies. High efficiency and low leakage current are observed.
Mining relationships between mental health, academic performance and human behaviour

The relationships among human behavior, mental health and academic performance have attracted researchers in psychology and education for many years. With the increase of the embedded sensor, the mobile phone can collect the data of the users' behavior from different aspects and different dimensions continuously and unconsciously. Focusing on behavioral relationship analysis, this paper presents a new method based on genetic algorithm to efficiently discover behavioral correlates of mental health (i.e., depression, stress, loneliness, self-perceived success) and academic performance. We conduct the experiments based on the open dataset called “StudentLife”. Ultimately, we discover interesting relationships which have never been considered before. We believe this approach provides a novel and effective way to mine massive relationships from mobile contextual data in the future.
Incremental adaptive semi-supervised fuzzy clustering for data stream classification

Data stream mining refers to methods able to mine continuously arriving and evolving data sequences or even large scale static databases. Most of data stream classification methods are supervised, hence they require labeled samples that are more difficult and expensive to obtain than unlabeled ones. Semi-supervised learning algorithms can solve this problem by using unlabeled samples together with a few labeled ones to build classification models. Recently we introduced a method for data stream classification based on an incremental semi-supervised fuzzy clustering algorithm. This method processes data belonging to different classes assuming that they are available during time as chunks. It creates a fixed number of clusters that is set equal to the number of classes. In real-world contexts a fixed number of clusters may not capture adequately the evolving structure of streaming data. To overcome this limitation in this work we extend our method by introducing a dynamic component that is able to adapt dynamically the number of clusters. Preliminary experimental results on a real-world benchmark dataset show the effectiveness of the dynamic mechanism introduced in the method.
Scalability of Wireless Fingerprinting Based Indoor Localization Systems

Fingerprinting indoor localization systems have been studied from different perspectives in the past decades; however, a vitally important piece in the puzzle is still missing: how does the system scale with the number of users? In this paper, we study the issue from a theoretical perspective, where the upper and lower bound of the system's localization reliability with respect to the number of users are derived. Our theoretical results can be verified by experiments thus can provide meaningful guidance for practical system design, which is in contrast to the traditional scaling-law work utilizing asymptotical analysis that is valid only under unverifiable extreme conditions. The theoretical and experimental results of our work reveal two interesting observations that shed light on the insight into the scalability of the fingerprinting localization system: First, the localization reliability drops dramatically before the number of users increases to a critical point and then decreases smoothly, where the critical point tends to appear when the number of users equals the number of access points (APs) deployed in the service region; second, even if the number of users approaches to infinity, the fingerprinting localization system still retains certain level of reliability.
Protein Flexibility in In Silico Screening

We summarize computational approaches in structure-based ligand design (SBLD) and in silico screening that address issues of protein flexibility and mobility. In particular, we consider how protein plasticity can be incorporated into docking strategies. As a first requirement, one needs to detect what can move and how. Moving protein parts can be identified from experimental information as well as established computational techniques such as molecular dynamics (MD) simulations, graph theoretical and geometry-based approaches, or harmonic analysis-based methods. Second, this knowledge needs to be transformed into a docking algorithm. A multitude of approaches considering protein mobility has been introduced recently, with motions modeled either implicitly or explicitly. In the latter case, one can further distinguish between modeling of side-chain-only motions and motions including backbone changes. In all cases, accuracy needs to be balanced against efficiency. Case studies for which the inclusion of protein plasticity was crucial to success are noted along these lines. This allows us to identify scope and limitations of the current approaches, as well as guidelines for further developments. Keywords: conformational selection; conformational variability; flexible docking; flexibility; induced fit; mobility; plasticity; protein–ligand
Two stage frequency offset pre-compensation scheme for satellite mobile terminals

In the mobile Satellite communication system, frequency offset will seriously affect the quality of communication. The automatic frequency control (AFC) technique is usually introduced in the satellite mobile terminals, to combat the frequency offset problem existing in the mobile communication. However, this technology has a poor correction performance in the case of crystal drifts. In order to solve this problem, we analyze the frequency offset factors in the process of mobile communication, Doppler frequency shift correction and crystal temperature drift compensation are separately carried out, and an improved two stage frequency offset pre-compensation scheme is proposed in this article, which can make up for the shortcomings of traditional methods and improve the overall performance of the system.