Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Direct and Indirect Load Control for Congestion Management in LV Networks

With the energy transition, capacity challenges are expected to occur more frequently in low-voltage (LV) distribution networks. In the literature, several direct and indirect load control methods have been suggested as solutions to alleviate network congestion. Direct methods involve the network operator directly controlling appliances at the households, while indirect methods aim to motivate end-users to shift their consumption through price changes. In this research, the direct and indirect methods are combined into an integrated approach, making use of the advantages of both methods. An agent-based architecture is adopted so that distributed and computational intelligence can be combined to ensure a smooth coordination among the actors. A sensitivity- based curtailment scheme is used to incorporate the unbalanced loading condition of the LV networks. The efficiency of the proposed integrated approach is investigated through simulations in the unbalanced IEEE European LV test feeder. Simulation results reveal up to 94\% reduction in congestion by the integrated approach, while maintaining the required levels of supply in the network.
Rapid prototyping of microfluidic devices and their applications

In this paper, rapid prototyping techniques for microfluidic master mold fabrication, using copper clads micromachining and 3D printing technologies are presented. Copper clad is a promising material for microfluidic mold fabrication due to its: smooth surface, availability of different copper and dielectric layer thicknesses, affordability and easiness of machining. In addition, 3D printing technology was investigated as a rapid prototyping technique to achieve appropriate master mold fabrication. Several molds with different channel features (width and height) were fabricated from each technique and were compared from the stand point of channel resolution, minimum space between channels, surface roughness, fabrication time and cost. Finally, microfluidic chips were fabricated using those molds by using molding technique based on Poly (dimethylsiloxane) (PDMS). The collection of these techniques offers precise channel heights starting from 15 m and different channel width ranging from 50 m in a short time fabrication process (i.e., 5min), without needing clean room equipment for molds fabrication.
Missing Structural and Clinical Features Imputation for Semi-supervised Alzheimer's Disease Classification using Stacked Sparse Autoencoder

In recent years, the accurate detection of Alzheimer's disease (AD) at its early stage, using various biomarkers through machine learning techniques, has been given paramount importance in the medical field. However, in reality, the input datasets contain lots of missing values due to several factors such as increasing mortality rate, avoiding invasive procedures, and dropping out from the study. In this work, after analyzing the pattern of structural and clinical data from tadpole study in Alzheimer's disease neuroimaging initiative (ADNI) database, it has been found that the unobserved data are not missing completely at random. In view of this fact, with the assumption that the missing data patterns are in blocks, we propose a novel stacked sparse autoencoder based method to assign a value in the missing places and to select the significant structural and clinical features in order to discriminate the patients having AD, mild cognitive impairment (MCI), and cognitively normal (CN) clinical status. Through experimental results, it is shown that the proposed imputation algorithm achieves better performance for semi-supervised AD classification in terms of accuracy, sensitivity, and specificity in 5-fold cross validation when compared to the state-of-the-art methods.
Topology-Aware Resource Allocation for IoT Services in Clouds

With the development of the Internet of Things (IoT), the cloud data centers have already been an important foundation to support IoT data analysis and data-driven IoT services. For the data-driven services provision, cloud resources are necessary for the service components in the form of virtual machines (VMs). At the same time, there is a frequent data transmission among the service components (or VMs). Hence, to reduce the IoT services’ response time, it is critical to improve the network issue and avoid network bottleneck during resource allocation. In this paper, we investigate the VM placement problem for balanced network utilization by avoiding network congestion. We first use the resource topology model to represent user requests and formulate the problem formally. We prove that the problem is NP-hard and present a heuristic algorithm based on the resource topologies. The core idea is to analyze the global and required resource topologies and place the required VMs into multiple servers with lower communication cost. We conduct extensive simulations, and the simulation results show that our algorithms have significant performance improvement on reducing network occupation and IoT service delay compared to the best-fit strategy and divide-and-conquer strategy.
Product Recommendation using Image and Text Processing

Production recommendation systems allow users to review other information that are relating to the product that they are interested in. The fundament of this problem in computer and technology perspective is to how extract information from the product that can be used for matching the related products. This work presents a technique that integrates information from production images and the description of the product (text format) to match a set of products collected in a databased. The matching will be used as the product recommendation system. Shape-based representation is extracted from the image. This includes HOG, Shape Context and Hu Moments. The description of the product is embedded by the information presented using LSTM technique. Integration between image and text information is performed using a simple weighting technique. The matching id carried out using cosine similarity measurement. The data is collected from online stores. The experimental results show that the proposed technique gives promising results.
A Rear-End Collision Risk Evaluation and Control Scheme Using a Bayesian Network Model

This paper presents a probabilistic decision-making framework for rear-end collision avoidance systems, focusing on modeling the impact of major collision-causing factors on the occurrence of accidents. Decisions on when and how to assist drivers are made using a Bayesian network approach according to collision risk evaluation results, given a prior probabilistic knowledge. The structure of the Bayesian network model is learnt using a K2 algorithm with a practical dataset. To provide adequate response time for drivers, we also predict collision probability in the next monitoring interval using a Kalman filter model. The prediction accuracy is evaluated with different use cases and compared to real scenarios with the obtained dataset. To make our framework more relevant to practical applications, we also discuss the corresponding safety control strategies by classifying collision risk into high and low levels. In addition, the proposed model is evaluated through experiments including simulations and road tests. In the simulations, the algorithms are tested in different scenarios with various configurations of weather conditions, driver response capability, and vehicular dynamics. In order to demonstrate collision avoidance performance, the proposed model is also compared to existing schemes. In the road tests, the algorithm is embedded into an unmanned vehicle with predefined parameters to evaluate the impact of computational complexity on rear-end collision avoidance. Numerical results show that the proposed model provides an accurate estimation for car-following collision risk with a relatively low complexity, taking into account the impacts of vehicle dynamics, driver reaction capacity, and external environment on rear-end collisions.
Test-Based Clone Detection: an Initial Try on Semantically Equivalent Methods

Most code clone detection approaches identify clones via static source code analysis. Such approaches are effective and efficient in detecting lexically similar clones. However, they are less effective in detecting semantic clones that are similar in functionality but different in implementation. As an initial try to detect semantic clones, in this paper, we propose a test-based approach to detecting methods that are semantically equivalent to API methods. For a given method ${m}$ , we generate its test cases automatically and search for semantically equivalent API methods by running the generated test cases. If two methods generate the same output on each of the test cases, they are taken as semantically equivalent methods. One of the weakness of test-based clone detection is that it is often time consuming. To reduce the time complexity, we take the following measures. First, we focus on methods instead of arbitrary fragments. Second, for a given method, we only compare it against such API methods whose signatures are highly similar to that of the given method. We evaluate the proposed approach on 10 well-known applications. Evaluation results suggest that it is efficient and accurate, and its precision is up to 98%.
A Conservative Prediction Model of Power System Transient Stability

With the emerging of Wide-Area Measurement System (WAMS) and Phasor Measurement Units (PMUs), their applications enable the online diagnosis and prediction of the operating status of power systems. Unlike most existing pattern recognition methods for transient stability prediction, a conservative prediction model for power system transient stability is proposed in this paper, aiming at improving accuracy when predicting the unstable cases. The model is established as an ensemble learning model using multiple Support Vector Machines (SVMs) as sub-learning machines. The generator power angle, rotor speed and the bus voltage amplitudes within a few fundamental cycles after the clearance of the fault, are used as the input features. A proof of the conservatism of the proposed method is also presented based on the conditional probability theory. Numerical tests with the New England 39-bus test system, show that the prediction accuracy for unstable cases is 98.26%, which are at least 2.5% higher than those from six comparative models. The proposed model is more conservative to provide reliable information for potential on-line stability or emergency control applications.
Latency Modelling in IEEE 802.11 Systems with non-IEEE 802.11 Interfering Source

Centralised wireless network management becomes increasingly common but requires information about wireless links to be able to properly use the existing spectrum. IEEE 802.11 employs the Distributed Coordination Function (DCF), which uses a decentralised Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) approach to control the access to the wireless medium. Both IEEE 802.11 and non-IEEE 802.11 sources can interfere with the transmission and degrade performance, but the impact of non-IEEE 802.11 interfering sources has not been extensively researched yet in literature. Only systems without a non-IEEE 802.11 interfering sources are considered in existing models. In this paper we explore the impact of non-IEEE 802.11 interference through realistic measurements in a large-scale wireless testbed. Based on these observations, we discuss the impact of an interfering source and identify the main components that affect the delay. We also propose an outline of a possible analytical model that we will extend in future work.
Mining Variable-Method Correlation for Change Impact Analysis

Software change impact analysis (CIA) is a key technique to identify the potential ripple effects of the changes to software. Coarse-grained CIA techniques such as file, class and method level techniques often gain less precise change impacts, which are difficult for practical use. Fine-grained CIA techniques, such as slicing, can be used to gain more precise change impacts, but need more time and space cost. In this paper, by combining the features of the coarse-grained technique and the fine-grained technique, a variable-method (VM) correlation-based CIA technique called VM-CIA is proposed to improve the precision of static CIA. First, the VM-CIA technique uses the abstract syntax tree (AST) of program to construct a novel intermediate representation called variable and method triple (VMT), which is used to analyze the correlation between the variables and methods. Second, the VM-CIA technique proposes the single-change impact analysis algorithm and multi-change impact analysis algorithm to compute the impact set based on the VMT representation. In addition, the VM-CIA technique can get a sorted impact set which is more accurate than the existing CIA techniques. The empirical results show that the VM-CIA technique can greatly improve the precision (19%) over traditional the CIA techniques, while at the cost of a little recall (5%). Moreover, the empirical studies also show that the VM-CIA technique predicts a ranked list of potential impact results according to the distance measure, which can greatly facilitate the practical use.
Privacy Preserving Scheme for Pseudonym Refilling in VANET

The privacy in VANET is an essential issue because the identity and the real-time location of the user can threat his safety if misused. To address this problem, many researchers focused on the use of certified pseudonyms and their change strategies to ensure the un-linkability and anonymity. IEEE 1609.2 Standard defined the certificates formats and pseudonym certifying process. However, it did not precise the communication procedure between the certifying authority and the requesting vehicle. In this paper, we propose a new privacy-preserving scheme for pseudonym on-demand refilling, in which the vehicle authenticates itself anonymously to the Regional authority subsidiaries of the central trusted authority to request a new set of pseudonyms. Our proposal is formulated using Burrows, Abadi and Needham (BAN) logic to prove its correctness, it is also specified and verified using SPAN (Security Protocol ANimator) and AVISPA (Automated Validation of Internet Security Protocols and Applications) tools. The logical demonstration showed that the authentication can be achieved while the privacy is preserved. The SPAN and AVISPA tools proved that it is secure against replay attack and session hijacking.
Aspects Oriented Approach towards the Bug Fixing Performance Lifecycle

Information technology and the development process have driven to dynamic approach towards the bug fix and its improvement. In the process of the software development with respect to time and trend, market has changed a lot in the context of the time and material based projects and many more. Software process development in terms of the agile where most of the client based or can be called service based organization have seen a drastically change where the client involved directly either the process making as scrum master. In the process of the development, we come across the bugs either from the testing or production environment. In this paper, we have tracked the each bug and corresponding solution; facilitates to increase the efficiency of bug fix. In that extend, we have given emphasis on the process of the governance in terms of the all the phases starting from the requirement gathering ending at the product delivery and maintenance, along with priority of bug which is distributed and make sense in terms of the quality and time with the state, behaviour and identity. Hence we have given emphasis on the part of the query based approach to rally like tool which make the process of the development on the distributed easier and simplified.
Bandwidth Constrained Distributed Inter-domain Path Selection (DIPS)

In this paper, bandwidth constrained path selection technologies are reviewed, which may utilize distributed routing protocols with appropriate extensions or centralized mechanisms based on Software Defined Network (SDN) to calculate end-to- end bandwidth constrained paths over Internet without using links with depleted bandwidth. In these traditional schemes, the resource reservation may sometimes fail due to inadequate bandwidth over running network links. Because of the requirements of scalability and privacy for routing policies, SDN-based technologies, such as B4, KANDOO and Control eXchange Point (CXP), are not perfect to solve this problem faced by path selection. In addition, distributed routing protocols with extensions, e.g., OSPF-TE, can only help calculate paths inside one particular autonomous system, which cannot satisfy the need of end-to-end path selection over whole internet. Based on these observations, the Distributed Inter-domain Path Selection (DIPS) scheme is then proposed in this paper. In which, all links without enough bandwidth for allocation are excluded from the network resource reservation pool. This DIPS scheme can generate bandwidth constrained end-to-end paths, which serve as a distributed solution for inter-domain path calculation, considering centralized or distributed intra-domain technologies. Finally, the proposed scheme is compared with existing solutions on bandwidth utilization and computational complexity.
Automatic Artifact Reduction Based on MEMD- for Seizure Prediction

The performance of seizure prediction is usually affected by various kinds of artifacts, especially by physiological artifacts. To improve the performance of seizure prediction, this paper proposed an automatic artifact reduction method based on multivariate empirical mode decomposition and independent component analysis (MEMD-ICA). The proposed method could identify electrooculography (EOG) and electromyographic (EMG) artifacts precisely while keeping the useful neural signals as much as possible. The performance of seizure prediction has been significantly improved with an accuracy of 90.59% and a sensitivity of 91.09% based on CHB-MIT database.
Object Detection for Soft Robotic Manipulation Based on RGB-D Sensors

In this paper, a visual recognition and location method using RGB-D information fusion is proposed for object detection, which is convenient for soft robotic manipulation to grasp objects. First, the environment is scanned and reconstructed by ORB-SLAM2, and the acquired color image and point cloud data are further processed, and the object feature database, which includes color and depth information, is constructed. Secondly, the point cloud in the point cloud library and the matching point cloud are put into the same coordinate system, and the ICP algorithm is used to match the point cloud to the point cloud in the point cloud library as much as possible, and the matching error is obtained, and the region of interest is obtained according to the size of the matching error. Then, if the region of interest is not uniquely determined, the object recognition is further realized by using the inception-v3 model and the transfer learning to identify the region of interest. After obtaining the only definite region, the position of the object relative to the camera is obtained through the correspondence between the color information and the point cloud data, and the object location is achieved. In order to verify the rationality of the method, a matching validation experiment was designed and a database containing multiple objects was established. In the experimental stage, the point cloud information and color information of the object are collected again from another angle, and they are matched with the point cloud in the point cloud library to get the results and analyze them. The results show that the error of the point cloud that belongs to the same object is much less than that of the point cloud that does not belong to the same object. Then the object can be identified and the location of the object can be identified successfully by color recognition.
1T-DRAM With Shell-Doped Architecture

This paper reports on the usefulness of shell-doped (SD) junctionless (JL) transistor architecture for operation as capacitorless dynamic random-access memory (1T-DRAM). SD topology overcomes the problem associated with shallower potential depth in heavily doped devices, thereby enhancing the retention time (RT) along with improved scalability. The use of a thinner shell for achieving high RT is beneficial as it reduces generation and recombination of holes. The results show that an undoped core region with shell thickness (<inline-formula> <tex-math notation="LaTeX">${T}_{\textsf {Shell}}$ </tex-math></inline-formula>) of 2 nm yields maximum retention. An SD (<inline-formula> <tex-math notation="LaTeX">${N}_{\mathrm {d}}$ </tex-math></inline-formula>) of <inline-formula> <tex-math notation="LaTeX">$10^{18}$ </tex-math></inline-formula> cm<inline-formula> <tex-math notation="LaTeX">$^{-\textsf {3}}$ </tex-math></inline-formula> attains RT of ~5.5 s and ~630 ms at 27 °C and 85 °C, respectively, whereas higher <inline-formula> <tex-math notation="LaTeX">${N}_{\textsf {d}}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$10^{19}$ </tex-math></inline-formula> cm<inline-formula> <tex-math notation="LaTeX">$^{-\textsf {3}}$ </tex-math></inline-formula> shows RT of ~13ms at 85 °C for <inline-formula> <tex-math notation="LaTeX">${L}_{\textsf {g}} = \textsf {200}$ </tex-math></inline-formula> nm. SD JL transistor shows less degradation in RT with temperature. A 10 nm SD JL device with RT of ~11 ms at 85 °C demonstrates applicability as 1T-DRAM at shorter gate lengths.
The Impact of Temperature on GaN/Si HEMTs Under RF Operation Using Gate Resistance Thermometry

Several optical and electrical methods have been developed to thermally characterize GaN high-electron mobility transistors (HEMTs) under DC operation. Gate resistance thermometry (GRT) has proven to be an inexpensive and reliable method to estimate the peak temperature. The complex Joule heating profile of a HEMT, however, is highly bias dependent, and its temperature profile under RF conditions may vary compared to the profile generated under DC operation. Evaluating the thermal performance of HEMTs under RF operation is thus necessary to quantify any differences between these two modes of operation. Implementing a differential probe, this paper highlights the capability of using GRT to measure accurately the temperature of GaN/Si HEMTs under RF operation. A larger gate-to-gate spacing is shown to decrease the overall temperature rise, while a linear correlation is found between the power-added efficiency and the junction temperature. The effect of the baseplate temperature on the device’s thermal performance both under steady-state and RF operations is presented. Overall, at a given power density and baseplate temperature, the RF temperature rise is shown to appear lower than its equivalent temperature under DC biasing.
Dynamic Generator State Estimation using PMU Measurements for Unknown Generator Input

Estimation of internal states of state space model of synchronous generators is very much essential in monitoring and control strategies based on modal analysis. Kalman Filter based techniques are used to estimate these states locally. Wide Area Power System control based on state space model of the system requires remote monitoring of states. Phasor Measurement Units (PMU) are widely used for remote monitoring and control, but the remote measurements are limited to output measurement like voltage, current, and frequency. Traditional Kalman Filter based techniques require both input and output measurements of the generator in order to estimate the states. This paper proposes the application of an input invariant filter technique for remote estimation of generator states using the limited PMU measurements, even in the absence of mechanical input torque and field voltage measurements. The performance of the filter under various grid conditions and in the presence of measurement errors are analysed in the paper.