Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fuzzy Logic Controller for Demand Side Management in Smart Grids

Smart Grid Demand Side Management is the effective way for energy providers to encourage their customers on reducing their consumption during peak loads through several Demand Response programs. In this paper, An Artificial Intelligence approach based on a Fuzzy Logic control system is proposed for the home appliance scheduling problem. This is typically used in Home Energy Management Systems for the control of Heating, Ventilation, and Air Conditioning Systems (HVAC). The simulation results demonstrate the capability of the proposed model to manage and control of HVAC systems in a smarter way than traditional techniques. Furthermore, a reduction of 18.33% in total hourly energy consumption has been obtained after introducing a new parameter among the fuzzy input variables.
CAD: Command-Level Anomaly Detection for Vehicle-Road Collaborative Charging Network

A large number of charging piles installed on roadside parking spaces and smart poles on the roadside of the Internet have become essential substation infrastructure (roadside) for building a vehicle-road coordinated charging network for electric vehicles. The management system of China National Grid’s network-load interaction includes the interaction between these main stations (traffic control stations) and substations (roadsides). The Internet-friendly interactive communication protocol for China’s vehicle-road coordination is IEC 60870-5-104 (104 protocol). The control network of the vehicle-road collaborative charging network has many characteristics, such as multiple levels, multiple types, and frequent information exchange for monitoring and control. Various types of operational information and control commands are subject to eavesdropping, tampering, and interruption during collection, transmission, and triggering. This paper proposes a command-level anomaly detection (CAD) method for a vehicle-road collaborative charging network. The CAD method analyzes the protocol for the specification format and business command characteristics of the 104 protocol. This paper uses the dynamic analysis protocol fuzzy test to realize the dynamic information in the program to guide the generation of test cases and pass the Markov state transition diagram. We describe the state transition and abnormality identification of protocol messages. We also design a long-term memory network to implement instruction-level anomaly feature mining. The experiment proved the validity of CAD. If we adopt other new protocols for the vehicle-road coordinated network in different countries or regions, the analysis of the new protocol can be completed in the same way, which has strong application value and prospect.
Magneto-division of Vesicle: Theory and Possible Experiments

Our theory has revealed the possibility that the vesicle, which is self-assembled by the diamagnetic amphiphilic block-copolymers, can be manipulated into division by external magnetic field. For the case of the constraint of constant surface area, the passive division can successively take place for 10 times in the situation of Δp = −15 mV; and only 4.5% solution that is contained by the original vesicle with a radius of 4 µm can been retained by 512 vesicles that each contains about 2.31×107 nm3. Thus, if the water channels are embedded in the membrane of vesicle, this method can not only concentrate the solution, but also produce a large number of nanoreactors, which is beneficial to yield an ensemble conclusion of chemical reaction in a very short times. Another case of the constraint of constant volume can also be easy realized by enough supply of the diamagnetic amphiphilic block-copolymers in the progress of division. The latter case is also important for reaction statistics because the original solution can be in equal volume divided into hundreds nanoreactors. This nanoreactor can be used to mimic the reaction of some organelles in vitro. We hope experimenters will try them in future experiments.
Box-Cox-type transformations for linear and logistic models with random effects

Random effect models have become a mainstream statistical technique over the last decades; and the same can be said for response transformation models such as the Box-Cox transformation. The latter ensures that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for the use of a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. The first aim of this thesis is to develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the “Nonparametric Maximum Likelihood” towards a “Nonparametric Profile Maximum Likelihood” (NPPML) technique. The implemented techniques allow to deal with overdispersion as well as two-level data scenarios in general linear models. The second part of this thesis considers the transformation of mixed-effects logistic models, with the aim of improving model fit. In binary data, link functions other than the logit can be used to connect predictors with the response. The Box-Cox transformation is used in mixed–effects binary regression models as an alternative link function for linearization purposes. The NPPML approach is used similarly as before, with some adjustments. The proposed approach is implemented in the R package boxcoxmix. Simulation studies and applications on real data are carried out to study the performance of this approach.
Monostatic Antenna In-Band Full Duplex Radio: Performance Limits and Characterization

This paper presents an in-band full duplex (IBFD) architecture, comprised of a monostatic antenna and software defined radio (SDR) implementing digital self-interference cancellation (DSIC). For this architecture, three alternative monostatic antennas with high passive suppression are integrated with a least squares linear DSIC algorithm implemented on the WARP v3 orthogonal frequency-division multiplexing based IEEE 802.11 SDR. Via detailed laboratory tests, the performance of the proposed IBFD radio architecture is investigated, considering various indoor positions and orientations for each antenna. The measurement results have been utilized to devise an analytical model for characterizing the power of the residual self-interference in the monostatic antenna IBFD radio as a function of transmit power levels. It is shown that the monostatic IBFD architecture can achieve up to almost 99 dB total cancellation, enabling medium range full duplex communication with moderate transmit power levels. Bidirectional communication is demonstrated between the two IBFD nodes with the proposed architecture, and error vector magnitude is observed as a function of increasing transmit power, i.e., SI. The throughput gain of full-duplex communication over half-duplex communication is also calculated as a function of payload size and the gain is found to be close to two for the settings used in the experiments.
A Critical Insight into Congestion Control Mechanism in WSN

The wireless network leads towards the unpredictable network load due to the event-driven nature of WSN. The sensors may densely distribute in different areas i.e. in the Forest, in militaries and etc. The sensor nodes carry all necessary components for transmission and reception of data packets. During the transmission, the data packet may be lost due to the congestion at single node. In this paper, we review, classify and compare the approaches of various congestion control mechanisms through which the issues have been fairly controlled based on assumptions and results provided by various researchers.
SPARK by Example: an introduction to formal verification through the standard C++ library

This paper presents SPARK by Example [10], a guide for people wanting to get involved in formal verification of SPARK programs. SPARK by Example is inspired by ACSL by Example, a similar effort for C/ACSL programs, and provides detailed specification, implementation and proof of classic algorithms (array manipulation, sorting, heap etc). A comparison between ACSL and SPARK is done in the light of proof performance and ease of use.
A Study of Smartphone Game Users through EEG Signal Feature Analysis

Recently, studies on different applications using measured brain waves have been increasingly progressing. It started out with studying brain waves of a monkey to control an arm with a motor. Then it is applied for raising children’s attention or understanding a patient situation. This paper deals with measuring the attention and meditation of EEG signal when a smartphone game user is playing. A survey will be conducted that measures the EEG signals for the smartphone game users while playing the game for analysis. The results will be depending on the memory and expressions of game users when solving hard problems during the game.
Multitask Learning of Time-Frequency CNN for Sound Source Localization

Sound source localization (SSL) is an important technique for many audio processing systems, such as speech enhancement/recognition and human–robot interaction. Although many methods have been proposed for SSL, it still remains a challenging task to achieve accurate localization under adverse acoustic scenarios. In this paper, a novel binaural SSL method based on time–frequency convolutional neural network (TF-CNN) with multitask learning is proposed to simultaneously localize azimuth and elevation under unknown acoustic conditions. First, the interaural phase difference and interaural level difference are extracted from the received binaural signals, which are taken as the input of the proposed SSL neural network. Then, an SSL neural network is designed to map the interaural cues to sound direction, which consists of TF-CNN module and multitask neural network. The TF-CNN module learns and combines the time–frequency information of extracted interaural cues to generate the shared feature for multitask SSL. With the shared feature, a multitask neural network is designed to simultaneously estimate azimuth and elevation through multitask learning, which generates the posterior probability for candidate directions. Finally, the candidate direction with the highest probability is taken as the final direction estimation. The experiments based on public head-related transfer function (HRTF) database demonstrate that the proposed method achieves preferable localization performance compared with other popular methods.
Process driven framework for augmented reality in a manufacturing environment

In many domains where VR has proven its value, augmented reality (AR) starts getting recognition as a viable alternative to its more mature predecessor. Unfortunately, it has been suffering from similar challenges that VR needed to overcome. Some application domains, e.g. manufacturing, pose additional barriers preventing AR for widespread. So far in manufacturing, AR applications have been built with a specific task in mind and were mostly prototypes. In this paper, we focus on an AR application for process control with an emphasis on a generic process structure. This approach would allow to reuse the AR application in a range of processes which could be easily defined by a limited number of parameters. To test this approach, we developed an AR application using off-the-shelf components and applied it to a machining process in an actual manufacturing center. We used up to thirteen parameters to define each step of the process and provide input to the algorithm driving the AR application. We established that the experience of developing a process-centric AR tool was feasible and allowed the user to leverage just-in-time information available through the AR display.
Resource Allocation and Performance Optimization in Wireless Networks

In the past few years, demand has kept increasing for higher data rates in wireless networks due to the increases in the numbers of mobile subscribers and multimedia applications. Femtocell and free space optics(FSO) networks, which both are important solutions to build next generation wireless networks, are two networks studied in this dissertation. In this dissertation, we adopt optimization approaches for network design in both femtocell and FSO networks. The first part of this dissertation provides an introduction to the background and related research issues of the femtocell and FSO networks. In the first two chapters, cell association, handover management and scheduling policies in two-tier femtocell networks are studied extensively. Cell association problem is to associate users to either a macro base station(MBS) or a femtocell base station(FBS). The objectives of cell association problem can be: 1) Maximizing total network throughput; 2) Achieving fairness among all users; 3) Balancing load among all BSs. Cell association and handover algorithms are proposed to achieve these objectives. In the following chapters, research is extended to Free space optical (FSO) networking, which is an attractive energy-efficient technology with applications ranging from high capacity military communications to “Last-mile” broadband access solutions. Topology control, spatial diversity techniques and adaptive transmissions in FSO systems are studied to mitigate weather turbulence which greatly degrades link performance. The problem of building a spanning tree when number of transceivers on each base station is limited in FSO networks is first studied. Then, the challenging problem of relay selection and power allocation in cooperative FSO network is investigated. Last but not the least, the problem of optical power allocation under power budget and eye safety
Segmentation Of Retinal Blood Vessels Using A Novel Fuzzy Logic Algorithm

In this work, a rule-based method is presented for blood vessel segmentation in digital retinal images. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. Diabetic retinopathy is the most common diabetic eye disease and a leading cause of blindness. Diagnosis of diabetic retinopathy at an early stage can be done through the segmentation of the blood vessels of retina. Many studies have been carried out in the last decade in order to obtain accurate blood vessel segmentation in retinal images including supervised and rule-based methods. This method uses eight feature vectors for each pixel. These features are means and medians of intensity values of pixel itself, first and second nearest neighbor at four directions. Features are used in fuzzy logic algorithm as crisp input. The final segmentation is obtained using a thresholding method. The method was tested on the publicly available database DRIVE and its results are compared with distinguished published methods. Our method achieved an average accuracy of 93.82% and an area under the receiver operating characteristic curve of 94.19% for DRIVE database. Our results demonstrated an average sensitivity of 72.28% and a specificity of 97.04%. The calculated sensitivity and specificity
The Informational Value of Segment Data Disaggregated by Underlying Industry: Evidence from the Textual Features of Business Descriptions

I examine a fundamental determinant of disclosure quality: how underlying data are disaggregated. For this, I create a measure of industry disaggregation, which is the extent to which segment disclosures are disaggregated based on underlying industries. To identify underlying industries, I apply a deep learning algorithm that extracts textual features from Item 1 business descriptions, in which firms are required to accurately describe their products and services. Industry disaggregation captures the disclosure of underlying industries and the adherence to industry-based disaggregation criteria. Consistent with capital markets being informationally segmented by industry, I find that industry disaggregation is negatively associated with analyst forecast error and dispersion, and positively associated with analyst following and information transfers among analysts and investors. These findings indicate that financial information is more informative, and thus of higher quality, when disaggregated by standardized criteria that achieve comparability and match the information-processing strategies of capital market participants.
A Study on the Application of WSN Positioning Technology to Unattended Areas

Wireless sensor network (WSN) is a kind of self-organized network that plays a critical role in the rescue and relief work of some unattended areas in recent years. With the application background of unattended areas, a positioning algorithm with high accuracy and coverage in the WSN is presented in this paper. First, in view of the complicated environment in unattended areas and demand for accurate positioning, this paper focuses on the ranging-based positioning technology, introduces double-hop ranging in the Euclidean algorithm into the 3D ranging process at the node, and proposes 3DL positioning algorithm on this basis. This proposed algorithm is able to achieve 3D positioning of network nodes and effectively enlarge the network positioning coverage by upgrading the positioned node with adequate accuracy to proxy anchor. Second, since unattended areas are mostly non-line-of-sight (NLOS) environments and node positioning accuracy is affected by NOLS factors, a 3DL-N positioning algorithm is proposed by introducing the way of making up for node positioning error based on the 3DL algorithm. The algorithm makes use of the relative positioning function of anchors to figure out the ranging correction parameter and node positioning error compensation value and adds it to the positioning process of the common to further improve its positioning accuracy. Finally, the simulation results indicate that the positioning algorithm proposed in this paper enjoys significant strengths in terms of positioning accuracy and coverage on the premise of consuming no higher node energy.
A Multi Criteria based Test Suite Optimization Framework

Re-testing the software with new features, new test cases are to be addedin the existing test suite that increases the test suite size significantly. The resulted test suite may contain obsolete and redundant test cases which are must be removed for effective regression testing. Previously, researchers have proposed different regression testing techniquesto get the optimal test suite. Combination of these techniques can significantly improve the result. This paper proposed the new optimization framework which works in three phases: test case analysis, test case clustering, and test suite optimization. Optimization further consists of the suitable combination of test suite reduction and prioritization.Rather than using only single coverage criteria, in this paper multi coverage criteria are incorporated for optimizing the test suite size. With the help of a simple program the performance of the proposed work has been assessed. A result shows that this work can be beneficial for testers to solve the regression testing problems.
Effects of Drivers’ Actions on Severity of Emergency Vehicle Collisions

Emergency vehicles (EVs) are used to provide essential services to society in the event of an emergency. Hence, crashes involving these vehicles are a concern, and investigating crash characteristics of EVs is the first step toward improving their safety. In particular, the contribution of any improper driver behavior or actions must be identified before (a) a program of behavior modification is recommended to authorities or (b) countermeasures are implemented to improve road safety, or both. This study attempts to identify the driver actions that contribute significantly to the severity of a crash involving at least one EV by using data from the Province of Alberta, Canada, for the period from 1999 to 2008. In addition, the impact of control variables formed from demographic, vehicle, environmental, and behavioral factors will also be explored. The results indicate that drivers’ violations of the road rules significantly contributed to increasing the severity of crashes. Non-EV drivers’ errors, nonrepairable damage to the vehicle, collision of an EV with a two-wheeler, and sun glare were some of the other variables that had significant influence on crash severity.
Effect of Frequency Level on Vibro-tactile Sound Detection

It has been shown that vibrotactile stimuli elicits sound perception either on their own or by enhancing otherwise inaudible sounds. For taking advantage of this phenomenon in the design of vibrotactile displays it is important to identify its properties with respect to the level of the excitation frequency. In this work, the effect of frequency levels on the ability of humans to perceive vibrotactile stimuli as sounds at the index fingertip is investigated. Eight subjects participated in the study which included comparison of sound and vibration versus sound only signals. It is shown that as hypothesized, there is a range of frequency in which the phenomenon under study seems to be most intense with maximum occurrence at 300 Hz.
Design and Control of a Grid-connected Seven Level Inverter for Photovoltaic Applications

This paper presents the design and closed-loop current control of a grid connected seven-level, 3-phase diode-clamped multilevel inverter for Photovoltaic (PV) applications. The proposed closed loop current control technique is based on the voltage-oriented proportional integral (PI) controller theory. The modulation technique used is level-shifted-carrier sinusoidal pulse width modulation (SPWM). The gain values of PI controller were selected to achieve good current quality and dynamic response. Grid synchronization was achieved by using a synchronous-reference frame phase-locked loop (SRF-PLL). Matlab/Simulink was used for the control system design and simulation. The simulation results show that a 1.34% total harmonic distortion (THD) of the output current was achieved which is within the allowable current distortion limits by international standards. The stability of the system was analyzed using pole-zero mapping and root locus.
Learning cross-protocol radiomics and deep feature standardization from CT images of texture phantoms

Radiomics has shown promising results in several medical studies, yet it suffers from a limited discrimination and informative capability as well as a high variation and correlation with the tomographic scanner types, CT (Computer Tomography) scanner producers, pixel spacing, acquisition protocol and reconstruction parameters. This paper introduces a new method to transform image features in order to improve their stability across scanner producers and scanner models. This method is based on a two-layer neural network that can learn a non-linear standardization transformation of various types of features including hand-crafted and deep features. A publicly available database of phantom images with ground truth is used where the same physical phantom was scanned on 17 different CT scanners. In this setting, variations in extracted features are representative of true physio-pathological tissue changes in the scanned patients, so harmonized between scanner producers and models. The recent success of radiomics studies has often been restricted to relatively controlled environments. In order allow for comparing data of several hospitals produced with a larger variety of scanner producers and models as well as with several protocols, features standardization seems necessary to keep results comparable.
IMAGE CLASSIFICATION USING MACHINE LEARNING TECHNIQUES.

M. Prasanna Lakshmi 1 , M. Venkata Rao 2 And V. Esther Jyothi 3 . 1. Department of Computer Applications, V.R. Siddhartha Engineering College, Vijayawada, India. 2. Department of Mathematics, V.R. Siddhartha Engineering College, Vijayawada India. Manuscript Info Abstract . Manuscript History Received: 24 March 2019 Final Accepted: 26 April 2019 Published: May 2019