How dangerous is your Android app? An evaluation methodology

Today, Photovoltaic (PV) systems are very popular for power generation because they enjoy the advantage of being pollution free abundant natural resource, low maintenance cost and noiseless operation. Photovoltaic (PV) systems needs maximum power point tracking (MPPT) to maximize the PV array power output which is independent of the temperature, irradiation coPIC18F452nditions and electrical characteristics of the load. The Research Work introduces photovoltaic MPPT system based on PIC18F452 to achieve maximum power tracking. Moreover, the combination of boost circuit and PIC18F452 microcontroller is used for tracking of the maximum power generation from PV system. Moreover, Research presents a practical implementation of Perturbation and Observation (P&O), Incremental Conductance (IncCon) and Fuzzy Logic algorithms to achieve MPPT control. These algorithms are widely used because of its low-cost and ease of realization. Proposed P&O, Incremental Conductance and Fuzzy Logic algorithms are implemented and tested under different loads, and the test results are analyzed and compared. The results show the performance of the Fuzzy Logic and Incremental Conductance algorithm in tracking MPP is better than the P&O algorithm and the experimental results of Fuzzy Logic and Incremental Conductance algorithm indicate the feasibility and improved system functionality with high-efficiency. Also, results concludes that the design can effectively accomplish the purpose of the maximum power tracking.

Design and implementation of a fuzzy logic (FL) coprocessor is presented in this paper. The main goal of a FL coprocessor is to speed up the operations typical for FL algorithms. Current implementations of FL algorithms are dedicated and general purpose. The main drawback of solutions based on dedicated hardware is lack of configurability. Software implementation on general purpose processors is slow for many applications. The basic components of the FL coprocessor presented in this paper are designed in Handel-C using Celoxica's DK Design Suite. The inference engine inside the FL coprocessor is a Mamdani-type engine and the FL coprocessor only supports trapezoid and triangular membership functions. With the DK Design Suite, area estimations are analyzed and throughput is increased by applying pipelining and parallelism transformations.

One of the features of a smart home is fire detection. There have been many developments in previous studies, but not many have implemented a detection system with the fuzzy logic method. Therefore, in this research we have developed a fire detection system that applies fuzzy logic methods and algorithms. We used Raspberry pi 3 as the embedded system, DHT-11 and MQ-2 sensors to detect fires. Detection results will be processed using a fuzzy system and the results will be notified through the WhatsApp application and monitored through the web. The test results show that the system that has been developed is running well and can be used as a fire detection system in smart homes.

Described in this paper are the design and implementation of a microcontroller-based fuzzy logic peak current-mode controller (PCMC) for dc-dc converters. A fuzzy logic PCMC boost converter operating in the continuous conduction mode has been designed using an 8-bit microcontroller. A one-chip solution has been achieved. No complex algorithm is required to estimate the controlled current by using the on-board peripherals of the microcontroller. The fuzzy logic algorithm uses a rule-based description of converter operation. By using rule-based look-up tables and other techniques, the fuzzy logic algorithm is implemented on the low-cost microcontroller successfully. Experimental results indicate successful operation of the microcontroller-based fuzzy logic PCMC power converter system.

Automotive technologies growing fast nowadays, as all know that electric vehicle is one of the most interesting field there. Higher efficiency, better performance, and eco-friendly technology are great values offered by this technology. Even though many researcher keep trying to increase those values offered. Some of them are trying to implement Fuzzy Logic Algorithm in the control system which drives the electric motor. Fuzzy Logic are implemented on top of the PID control system so the Kp, Ki, and Kd parameters will be changed based on the feedback of the controller. This paper provides different Fuzzy Logic Algorithm, instead of implemented it on top of PID control system, Fuzzy Logic Algorithm’s output will be calculated directly for driving electric motor which is axial BLDC type of electric motor in this case. The experimental results show that implementing Fuzzy Logic directly is much better than previous approach.

The Android operating system, dominating over 85% of the mobile market through open-source flexibility, suffers from intrinsic vulnerabilities. The APK(Android Package Kit) parsability and Smali code modifiability enable attackers to decompile applications via tools. This facilitates widespread repackaging-malicious actors inject payloads or tamper with functionality, redistributing counterfeit applications(apps) through third-party markets. These practices cause dual damage. Developers face code theft and revenue diversion, while users endure privacy leaks, financial fraud, and device compromise. Consequently, accurate repackaging detection has become critical. This paper reviews recent progress in repackaging detection techniques for Android applications. We first outline the fundamental characteristics of Android apps and then examine detection methods based on code analysis and resource similarity. Frequently used Android app datasets and evaluation metrics for measuring the effectiveness of repackaging detection methods are also summarized. Finally, we discuss the development trends of repackaging detection techniques and identify future research directions, with the aim of providing meaningful insights and guidance for researchers in this domain.

In this article, a speed control of DC motor is designed and illustrated using fuzzy logic-based programmable logic controller (PLC). The DC motor is an attractive part of electrical equipment in many industrial applications requiring variable speed and load specifications due to its ease of controllability. The designed system is consisted of three main parts including programmable logic controller, pulse width modulation (PWM) bipolar drive and DC motor. In the control section, PLC is used as real time controller and fuzzy logic algorithm is designed based on nonlinear model of DC motor, and its parameters are optimized in MATLAB software. Then, it is implemented using rslogix5000 PLC and programming language ladder for speed control. Finally, with favorable results, the efficiency of the controller is successfully proved under different load conditions. The obtained results demonstrate the efficiency of the PLC intelligent controller in enhancing the accuracy and speed control of DC motor.

The present study proposes a decision-making model based on different models of driver behavior, aiming to ensure integration between road safety and crash reduction based on an examination of speed limitations under weather conditions. The present study investigated differences in road safety attitude, driver behavior, and weather conditions I-69 in Flint, Genesee County, Michigan, using the fuzzy logic approach. A questionnaire-based survey was conducted among a sample of Singaporean (n = 100) professional drivers. Safety level was assessed in relation to speed limits to determine whether the proposed speed limit contributed to a risky or safe situation. The experimental results show that the speed limits investigated on different roads/in different weather were based on the participants’ responses. The participants could increase or keep their current speed limit or reduce their speed limit a little or significantly. The study results were used to determine the speed limits needed on different roads/in different weather to reduce the number of crashes and to implement safe driving conditions based on the weather. Changing the speed limit from 80 mph to 70 mph reduced the number of crashes occurring under wet road conditions. According to the results of the fuzzy logic study algorithm, a driver’s emotions can predict outputs. For this study, the fuzzy logic algorithm evaluated drivers’ emotions according to the relation between the weather/road condition and the speed limit. The fuzzy logic would contribute to assessing a powerful feature of human control. The fuzzy logic algorithm can explain smooth relationships between the input and output. The input–output relationship estimated by fuzzy logic was used to understand differences in drivers’ feelings in varying road/weather conditions at different speed limits.

There is currently no universally accepted approach to weaning patients from mechanical ventilation, but there is clearly a feeling within the medical community that it may be possible to formulate the weaning process algorithmically in some manner. Fuzzy logic seems suited to this task because of the way it so naturally represents the subjective human notions employed in much of medical decision‐making. The purpose of the present study was to develop a fuzzy logic algorithm for controlling pressure support ventilation in patients in the intensive care unit, utilizing measurements of heart rate, tidal volume, breathing frequency and arterial oxygen saturation. In this report, we describe the fuzzy logic algorithm and demonstrate its use retrospectively in 13 patients with severe chronic obstructive pulmonary disease, by comparing the decisions made by the algorithm with what actually transpired. The fuzzy logic recommendation agreed with the status quo to within 2 cm H2O an average of 76% of the time, and to within 4 cm H2O an average of 88% of the time (although in most of these instances no medical decisions were taken as to whether or not to change the level of ventilatory support). We also compared the predictions of our algorithm with those cases in which changes in pressure support level were actually made by an attending physician, and found that the physicians tended to reduce the support level somewhat more aggressively than the algorithm did. We conclude that our fuzzy algorithm has the potential to control the level of pressure support ventilation from ongoing measurements of a patient’s vital signs. Abstract reprinted from the American Journal of Respiratory and Critical Care Medicine, volume 160, Nemoto T et al., ‘(1999) Automatic control of pressure support mechanical ventilation using fuzzy logic.’, pages 550–556. © 1999, reproduced with permission from The American Thoracic Society.

Adaptive complementary filter using fuzzy logic and simultaneous perturbation stochastic approximation algorithm

Design of efficient control algorithms for autonomous mobile robot movement in unknown and changing environment with obstacles and walls is a difficult task. There exist different strategies to design control systems to perform the robot movement. In this article possibility to use fuzzy logic, if-then rules, and genetic algorithm for autonomous mobile robot control is presented. Control using fuzzy logic is softer and better then control using if-then rules because of absence of motors speed jumps. It is shown that the rough control system can be designed using an expert knowledge. Then genetic algorithm can be used to improve the quality of the control system.

This paper presents a fuzzy logic strategy for the current control of a three-phase voltage source power PWM-inverter. The base of this regulator is a direct digital predictive loop which has a very good dynamic performance, but the steady state performance will heavily depend on the accuracy of the model structure and plant's parameters. To reduce the steady errors and compensate the system uncertainties, a feedforward control loop and a fuzzy logic algorithm are introduced. The direct digital predictive loop with the feedforward loop, which can be seen as a hybrid current regulator, generates the command voltage based on the nominal system conditions, guarantees the system with a very fast dynamic response and a smaller system error. The fuzzy logic algorithm, by measuring the amplitude and phase angle of the system error, generates a modifying signal to correct the gains of the direct digital predictive loop and the feedforward loop. When the difference between the reference and actual signals is larger than a given threshold, therefore it minimizes the effects of system uncertainties. Simulation results are presented to verify the effectiveness of the current regulation algorithm.

Intelligent Scanning Electrochemical Microscopy Tip and Substrate Control Utilizing Fuzzy Logic

Water pollution is one of the main reasons for the deterioration of the earth's environment.It directly affects people's water supply and would have serious consequences for public health.Emergencies often occur in water pollution projects.Because of its uncontrollability, it brings great inconvenience to non-pollution treatment, thus affecting the development of water resources.In order to effectively avoid the risk of emergencies, this paper used fuzzy logic algorithm to evaluate the risk of water pollution engineering emergencies.This paper first introduced the harm of water pollution engineering emergencies, and then analyzed the process and control strategy of water pollution engineering emergencies.After that, the emergency dispatching scheme of water pollution project was analyzed.Finally, the risk of water pollution engineering emergencies was evaluated, and the feasibility conclusion was finally reached.Compared with the general manual evaluation method, the fuzzy logic evaluation method is more accurate in the classification of emergencies.The fuzzy logic algorithm has a wide application space in the risk assessment of water pollution engineering emergencies.

Today, there's a synergy beginning to form among neural nets (NNs), fuzzy logic (FL) and genetic algorithms (GAs). This paper reviews developments in this respect. Many designs of knowledge-based and associated learning systems using the combination of NNs and FL abilities have been presented. Some methods for the integration of GAs with fuzzy systems are described. Afterwards, work on hybrid systems of GAs and NNs is discussed. Finally, some advantages obtained through the fusion of the FL, NNs and GAs are emphasized, and possible ways of combining the three are developed.