Wrong Input Research Articles

Resilient Cruise Control of Heterogeneous Platoons Against Byzantine Attacks: Theory and Experiment.

This article studies the problem of the resilient cruise control in heterogeneous vehicle platoons against f -local Byzantine attacks (BAs). Agents under BAs become traitors of the swarm, who try to mislead its neighbors while adopting wrong inputs. Thus, BAs are extremely challenging to be suppressed. This study introduces a novel hierarchical protocol characterized by a virtual twin layer (TL), motivated by the rationale of digital twin. This protocol separates the defense scheme against f -local BAs into two parts: one defense scheme against Byzantine edge attacks (BEAs) via the TL and another scheme against Byzantine node attacks (BNAs) via the cyber-physical layer (CPL). The TL employs a trusted-edge strategy, enhancing the network resilience by incorporating a minimal fraction of the key edges. It is rigorously proven that a TL topology meeting strong (2f+1) -robustness is sufficient for achieving distributed resilient estimation against BEAs. On the CPL, a series of decentralized chattering-free controllers is proposed, guaranteeing the resilient cruise tracking of heterogeneous platoons against exponentially unbounded BNAs. Besides, these controllers can achieve uniformly ultimately bounded convergence. The theoretical results' effectiveness and practicality are validated through a numerical simulation example and an unmanned ground vehicle experiment involving heterogeneous platoons against f -local BAs.

Artificial Intelligence-Based Brain Tumor Segmentation Using Adaptive Hybrid CNN and Classification by Multi-Scale Dilated MobileNet with Attention Mechanism for MRI Images

In the medical field, the demanding task is predicting the type of cancer cell, and the analysis to reduce the death rate. MRI technique is used for capturing the brain image because of its high-quality performance and non-ionizing radiation. These collected input images carry only the area of tissues as bright and remove the cerebrospinal fluid to increase the quality of the image. The supervised learning method indicates marvelous performances in solving the problems related to the segmentation and detection of medical images. In the existing model, Convolutional Neural Network (CNN) is used, and the execution of CNN is carried out in different layers. This network consists of different layers that lead to various challenges, such as execution cost and time being high, fetching the wrong input, arising misdiagnosis of the disease. Prediction using different datasets is difficult and creates an overfitting problem, and large memory is needed in the existing method. To overcome this entire issue, an advanced model is developed. Input images collected from online sources are given to the Brain tumor segmentation stage, where the Adaptive Hybrid Convolutional Neural Network (AHCNN) is utilized. Here, the TransResUNet and Fully Convolutional Network (FCN) is utilized to construct the AHCNN for effectively segmenting brain tumors. Here, the parameters are optimized using Flock Member Movement-Based Hybrid Coati with Tomtit Flock Optimization (FMM-HCTFO). The TransResUNet and FCN outcomes are fed to the prediction stage. Here, the brain tumor classification is done using the Multi-scale Dilated Adaptive MobileNet with Attention Mechanism (MSDM-AM) technique. Here, also the parameters are optimized using FMM-HCTFO. Finally, the brain tumor will be classified using the MSDM-AM technique. The achievement of the developed model for the prediction of the brain is increased by tuning the variables using the proposed algorithms.

A Scoping Review: Faktor Penyebab Pending Klaim BPJS Kesehatan Rawat Inap di Rumah Sakit

Pending claims for Indonesian Universal Health Coverage (BPJS Kesehatan) is a classic issue in the field of health budgeting in the National Health Security (JKN) era. Some studies have examined factors causing pending claims, however, the issue has not been figured out yet because every hospital has different problems. This study was conducted to identify the problems possibly causing pending claims faced by hospitals. The objectives of the study were to find out the factors causing inpatient pending claims for BPJS Health Insurance at the hospital. The article search method begins by identifying research articles on two databases, Google Scholar and Academia.edu with predetermined keywords. Articles are selected by PRISMA and based on established inclusion and exclusion criteria. The search results obtained 191 articles identified from two database searches, including 151 articles from Google Scholar and 40 articles from Academia.edu, finally, only 9 articles were obtained that matched the criteria. Pending claims for inpatient BPJS Health Insurance at the hospital under study were caused by several problems including inaccuracy in coding, incomplete administrative files, not attaching the results of supporting examinations as a support for the diagnosis, enforcement of diagnoses that did not meet the criteria, different perceptions of standard enforcement of diagnoses between specialist and BPJS, different points of view regarding the diagnosis code between hospital coding officers and BPJS verifiers, the discrepancy between main diagnoses and specialist, incomplete medical resumes, there is no SOP for managing BPJS Health insurance claims, knowledge of claim executors, facilities and infrastructure in the claims section, wrong input on the claim application. Managerial support is tremendously required to solve the issues related to pending claims for BPJS Kesehatan.

Electret Molecular Configuration Induced Programmable Photonic Memory for Advanced Logic Operation and Data Encryption

AbstractNonvolatile organic photonic transistor (OPT) memories have attracted widespread attention due to their nondestructive readout, remote controllability, and robust tunability. Developing electrets with similar molecular structures but different memory behaviors and light‐responsive features is crucial for light‐wavelength‐modulated data encryption. However, reported OPT memories have yet to meet this challenge. Here a new electret molecule (“H‐PDI”) is developed via reconfiguring the linear perylene diimide molecule (“L‐PDI”) to a helical shape. Respectively incorporating H‐PDI and L‐PDI into the floating gate layer results to H‐PDI OPT and L‐PDI OPT. Attributing to their remarkably different electronic structures and energy bandgaps, H‐PDI OPT and L‐PDI OPT preferably respond to 405 and 532 nm light irradiation, respectively. Upon electrical programming, data can be written and stored in both memories with good retention features and a high “1”/“0” state current ratio over 105, though the data can only be erased by light with correct wavelengths, rather than the electrical field. Moreover, data stored in a memory array consisting of both H‐PDI OPT and L‐PDI OPT can only be read out by correct inputs, and wrong inputs will lead to highly deceptive outputs. This study provides a general design strategy of OPT for advanced data encryption and protection.

A fast data-driven topology identification method for dynamic state estimation applications

This paper proposes a fast topology identification method to avoid estimation errors caused by network topology changes. The algorithm applies a deep neural network to determine the switching state of the branches that are relevant for the execution of a dynamic state estimator. The proposed technique only requires data from the phasor measurement units (PMUs) that are used by the dynamic state estimator. The proposed methodology is demonstrated working in conjunction with a frequency divider-based synchronous machine rotor speed estimator. A centralized and a decentralized approach are proposed using a modified version of the New England test system and the Institute of Electrical and Electronics Engineers (IEEE) 118-bus test system, respectively. The numerical results in both test systems show that the method demonstrate the reliability and the low computational burden of the proposed algorithm. The method achieves a satisfactory speed, the decentralized approach simplifies the training process and the algorithm proves to be robust in the face of wrong input data.

An FPGA application of home security code using verilog

Traditional entrance keys performed a number of drawbacks, including the ease with which they can be stolen, duplicated, or misplaced, allowing unauthorized people to gain access to cash, valuables, and other important documents. As known, most digital electronic entrance locks use application specific integrated circuits (ASICs) as based, which have the disadvantage of being unable to be reconfigured, as opposed to field programmable gate arrays (FPGA). This project proposed a home security code lock using verilog hardware descriptive language (HDL) with two unlocking modes, using a button or a keypad which can be changed using a switch. The 7-segment on the Altera DE2-115 trainer board is used to display the passcode press by the keypad. The result of simulation on the keypad using finite state machine (FSM) technique was fulfill the theoretical concept in which it will go to the next state each time the correct input or passcode was entered. When the wrong input or passcode was entered, it will be entered to reset mode. As the conclusion, a fully comply output according to the theoretical FSM concept is fully achieved in this project.

What lessons can be learned from cost efficiency? The case of Swedish district courts

Many studies have reported findings regarding technical efficiency (i.e., resource use) for district courts around the world. However, our review of previous research did not find any studies targeting cost efficiency. To fill this gap, this study investigates the cost efficiency of Swedish district courts. We apply the Farrell framework and decompose cost efficiency into allocative and technical efficiency. The study finds substantial cost inefficiency for district courts in Sweden, which is mainly due to allocative inefficiency (i.e., having the wrong input mix or paying too much for inputs). This result is not surprising since, for example, there is regional heterogeneity in input prices (i.e., wages and rents). However, it also reveals the potential of, for example, moving district courts out of city centres and high-priced areas. On the other hand, the location and staffing of district courts serve other competing policy concerns, such as the proximity of citizens to district courts. The cost-efficiency analysis gives information about the cost of these competing policy concerns.

Multi-Purpose Cyber Environment for Maritime Sector

The cyber attack surface in a maritime environment is constantly growing. More current information and computer technologies are being used on cargo and passenger ships to save on operational costs and increase navigational safety. Along with the growing reliance on automation, the risk of a disruption to a vessel's critical systems by drawing on the wrong inputs from sensors to change the behaviour of the actuators has significantly increased. Traditional operational technological systems are much more complicated to update than the automatic software updates we see in information technology systems. To better understand existing cyber threats in the maritime sector and increase cybersecurity resilience, this paper aims to replicate the digital components of a ship's bridge to examine scenarios when the bridge system loses connectivity, receives the wrong inputs from sensors, or the internal system becomes compromised. The simulator differentiates fundamentally from traditional simulators or digital twins in the maritime sector that focus on training seafarers. This environment generates data streams that are similar to those on board a ship. Those data streams can be analysed, modified and spoofed to observe the effects. The effects can be technical but it is equally necessary to analyse how human beings would react in specific circumstances. Our work provides the opportunity to isolate the ship network traffic, conduct penetration testing, find cybersecurity vulnerabilities on devices, and execute cyber attacks without the dangers associated with running such scenarios on a vessel in the open sea.

Deep learning models to predict flood events in fast-flowing watersheds

This study aims to explore the reliability of flood warning forecasts based on deep learning models, in particular Long-Short Term Memory (LSTM) architecture. We also wish to verify the applicability of flood event predictions for a river with flood events lasting only a few hours, with the aid of hydrometric control stations. This methodology allows for the creation of a system able to identify flood events with acceptable errors within several hours' notice. In terms of errors, the results obtained in this study can be compared to those obtained by using physics-based models for the same study area. These kinds of models use few types of data, unlike physical models that require the estimation of several parameters. However, the deep learning models are data-driven and for this reason they can influence the results obtained. Therefore, we tested the stability of the models by simulating the missing or wrong input data of the model, and this allowed us to achieve excellent results. Indeed, the models were stable even if several data were missing. This method makes it possible to lay the foundations for the future application of these techniques when there is an absence of geological-hydrogeological information preventing physical modeling of the run-off process or in cases of relatively small basins, where the complex system and the unsatisfactory modeling of the phenomenon do not allow a correct application of physical-based models. The forecast of flood events is fundamental for correct and adequate territory management, in particular when significant climatic changes occur. The study area is that of the Arno River (in Tuscany, Italy), which crosses some of the most important cities of central Italy, in terms of population, cultural heritage, and socio-economic activities.

Incorrect problem analyzing in the Finite Element Method

Nowadays, numerical methods are widely used in the field analysis of many electromagnetic devices, MEMS and more. Incorrect problem analysis using the Finite Element Method often leads to incorrect results and drawing wrong conclusions. Therefore, the role of the engineer who describes the model in the pre-processing stage cannot be forgotten. This paper presents an example of developing a computer model of a flat capacitor in FEMM program, using the 2-dimensional Finite Element Method. It has been shown, how simple can disturb the result after wrong input data implementing.

A Sensor Fault-Tolerant Accident Diagnosis System.

Emergency situations in nuclear power plants are accompanied by an automatic reactor shutdown, which gives a big task burden to the plant operators under highly stressful conditions. Diagnosis of the occurred accident is an essential sequence for optimum mitigations; however, it is also a critical source of error because the results of accident identification determine the task flow connected to all subsequent tasks. To support accident identification in nuclear power plants, recurrent neural network (RNN)-based approaches have recently shown outstanding performances. Despite the achievements though, the robustness of RNN models is not promising because wrong inputs have been shown to degrade the performance of RNNs to a greater extent than other methods in some applications. In this research, an accident diagnosis system that is tolerant to sensor faults is developed based on an existing RNN model and tested with anticipated sensor errors. To find the optimum strategy to mitigate sensor error, Missforest, selected from among various imputation methods, and gated recurrent unit with decay (GRUD), developed for multivariate time series imputation based on the RNN model, are compared to examine the extent that they recover the diagnosis accuracies within a given threshold.

EXTES: An Execution-Time Estimation Scheme for Efficient Computational Science and Engineering Simulation via Machine Learning

In recent years, computational science and engineering (CSE) simulations using high-performance computing resources are actively exploited to solve complex domain-specific problems. Thanks to the remarkable advance of IT technology, the CSE community is challenging more complex and difficult problems than ever before, by running these simulations online. In this regard, we often witness that 1) online simulation users suffer from knowing little about the estimated termination time of their launched simulations and 2) the limited computing resources are squandered by wrong input that leads the simulations to run forever. To address such issues, we propose a novel execution time estimation scheme, termed EXTES , using machine learning techniques for more efficient online CSE simulations. With a large amount of existing provenance data, the EXTES scheme trains a suite of models rooted from classification, regression, and a hybrid of the two and utilize these models to estimate the execution time for specified input parameters for simulations. In the experiments on real simulation data, our proposed models achieved about 73% accuracy on average in execution time estimation across 16 simulation programs taken from a variety of CSE fields. In the meantime, the overhead incurred by the training and estimation is almost negligible.

Smart Sensing Based on DNA-Metal Interaction Enables a Label-Free and Resettable Security Model of Electrochemical Molecular Keypad Lock.

Recently, molecular keypad locks have received increasing attention. As a new subgroup of smart biosensors, they show great potential for protecting information as a molecular security data processor, rather than merely molecular recognition and quantitation. Herein, label-free electrochemically transduced Ag+ and cysteine (Cys) sensors were developed. A molecular keypad lock model with reset function was successfully realized based on the balanced interaction of metal ion with its nucleic acid and chemical ligands. The correct input of "1-2-3" (i.e., "Ag+-Cys-cDNA") is the only password of such molecular keypad lock. Moreover, the resetting process of either correct or wrong input order could be easily made by Cys, buffer, and DI water treatment. Therefore, our system provides an even smarter system of molecular keypad lock, which could inhibit illegal access of unauthorized users, holding great promise in information protection at the molecular level.

MANUFACTURING OPTIMIZATION OF EDM ELECTRODE

Manufacturing of aluminum casting die in tooling industry is very lengthy and cumbersome process. The manufacturing processes always flow in the fixed sequence. These activities cannot be altered because these are placed in increasing order of metal removal. Any wrong input at any stage may create may lead to creation of the extra manufacturing activities in the next lined up stages. This creates a bottle necks, choking of the system which finally to lead increase in the manufacturing rope length of the die. Total summation of increase in the manufacturing rope length will finally kill the buffer period, paying penalty for not completing and dispatching on time.

스마트폰을 이용한 가스검출시스템 및 검출 방법연구

PURPOSE: A gas detection system using a smart phone and a gas detection method are provided to solve problems which are generated by an artificial manipulation or a wrong input by using a attachable/detachable sensor module and an application to the smart phone, and are provided to enhance the reliability of the data by enabling the measurement data, which is measured from the sensor module which performs the gas detection, processed as the expertise data about the combustible gas or the toxic gas, by sending to an operation server. CONSTITUTION: A gas detection system using a smart phone comprises a sensor module(20) and a gas detection APP(100). The sensor module is attached to the smart phone and detects and measures the combustible gas among the LNG, LPG, CNG, and Freon, and the toxic gas between the carbon monoxide, and the carbon dioxide. The gas detection APP has the following properties: installed inside the smart phone; a tag which is built in the smart phone is read by an RFID or NFC(Near Field Communication) which indicates a user or location for the gas detection by using a connection unit which stores an RFID reader or an NFC reader within the smart phone; and detects the combustible gas or the toxic gas and obtains the measurement data through the measurement by driving a sensor module after recognizing the user information and location information automatically. The smart phone transmits the user information, the location information, and the measurement data which are obtained from the gas detection APP to an operation server(40) through a communication network(30). The smart phone includes a database(50) which stores the user information, the location information, and the measurement data received from the operation server. [Reference numerals] (20) Sensor module; (30) Communication network; (40) Operation server; (50) Database

Young׳s Modulus evaluation using Particle Image Velocimetry and Finite Element Inverse Analysis

Most of the conventional design solutions using the Finite Element Method (FEM) have the material׳s properties defined. This is necessary to set those properties from commercial software library to run the simulations. However, some material׳s properties are not standardized which can provide unreliable results due to wrong input data to the simulations. In this case, non-destructive mechanical tests for measuring deformations can be used to generate displacements values in order to provide the material behavior through FEM inverse analysis methods. This paper is focused on using Particle Image Velocimetry (PIV) together with the Particle-Swarm Optimization (PSO) algorithm and the FEM inverse analysis to investigate Young׳s Modulus of the material ASTM A36 steel. The displacements of a cantilever beam were measured by means of PIV association to the speckle patterns provided by a laser beam. The results indicated Young׳s Modulus estimation error around 5% compared to the original material properties. It shows the potentiality of PIV associated to PSO in order to determine the mechanical properties of steel via the FEM inverse analysis in a robust and low cost procedure.

Geometrical variations of tubes and their impact on freeform bending processes

In bending processes for tubes and profiles, the produced bending geometry depends on the characteristics of the semifinished product. Tubes are subjected to scatter and deviations from the standard’s dimensions caused by their fabrication process. This results in deviations of the forming behaviour. Depending on the bending process, the effect of this phenomenon is even larger, if the deviation of process forces leads to a significant deflection of the die elements. Particularly kinematic bending processes, e. g. the 3-roll-push-bending process have shown to be highly sensitive to low machine stiffness. The effect of this factor has not been identified among the influences of other disturbance factors so far. As an additional aspect, the tube dimensions affect the material characterization which leads to inaccurate material properties in numerical simulations. This results in wrong input data for the process design and has to be corrected in lengthy adjustment procedures. In this contribution the scatter of some geometrical tubes properties and its impact on the 3-roll-push-bending process is investigated. The tube geometry is measured by a tactile measurement system and is compared to the product standards. Observed deviations are categorized according to DIN 4760. The impact of the deviations on the bending process is examined by numerical variant calculations. Besides the tube dimensions, the stiffness of the machine is varied and the influence determined. By considering the errors resulting from wrong input data in the material characterization the overall error is quantified.

Audio-Tactile Feedback Mechanisms for Multi-Touch HMI Panels of Production Engineering Systems

Over the last decade, touch screen interaction has been gaining wide acceptance in information technology and daily consumer products. Accordingly, first approaches to applications and devices for production engineering systems are now entering the market. Although they employ intuitive user concepts, touch screens for industrial HMI panels still lack haptic feedback. Since operators observes the machining process and machine handling is often done blind, false handling or wrong input signalsmay damagemachines or injure human workers. With this in mind, this paper presents a haptic feedback mechanism for touch-based interaction. A user evaluation performed on the developed system unveils increasing input security and thus an enhanced user experience.

User friendly application of RODOS

The decision support system RODOS (Ehrhardt and Weis, 2000) contains a large set of simulation models, data bases, user interfaces and system functionalities. However these many options resulted in heavy-weight and complex user interfaces difficult to handle by non-experts. For this reason a user friendly and light-weight access to the RODOS functionalities was requested by the end users. This was fulfilled by the design and implementation of a new user interface named RODOS-Lite. The main purpose of this redesigned user interface is to gently lead operators through a sequential line of input masks, hiding rarely used parameters (defaults are set) from the operator but still providing the possibility to access them if necessary. Additionally, the interface is strongly safeguarded to prevent operators from entering wrong input values. This is achieved by systematically checking input values and even suggesting occasionally best estimate parameters. RODOS-Lite is complemented by various simple but nevertheless extremely useful features such as online access to Google maps2 or source term visualisation.

사용자 익명성을 제공하는 스마트카드 기반 3자 참여 키 교환 프로토콜

2006년에 공개키 연산 대신 XOR 연산과 해쉬함수만을 사용하는 스마트카드를 이용하는 3자 참여 인증된 키 교환 프로토콜이 제안되었다. 최근에는 개인프라이버시에 대한 관심이 높아지며 사용자 익명성을 보호하기 위한 연구가 진행되고 있다. 본 논문에서는 2006년 제안된 3자 참여 인증된 키 교환 프로토콜이 사용자 익명성을 제공하지 못하며 잘못된 입력값 감지가 늦다는 문제점을 제기하고, 이러한 문제점을 해결하기 위해서 스마트카드 기반 3자 참여 키 교환 프로토콜을 제안하였다. Three-party authenticated key exchange protocol based on smartcards using XOR and hash function operation instead of the public key operation has been proposed in 2006. Recently, it is doing for research because of increasing interest in privacy. This paper pointed out that proposed three-party authenticated key exchange protocol in 2006 has some problems; it is user anonymity and slow wrong input detection, and then we proposed new one to overcome these problems.