Problem Of Error Research Articles

Lattice-based multi-authority ciphertext-policy attribute-based searchable encryption with attribute revocation for cloud storage

Multi-authority attribute-based searchable encryption (MABSE) is an flexible and efficient way to securely share and search encrypted data. Compared with single-authority systems, MABSE has more complex access control policies and key management mechanism. However, most existing MABSE schemes rely on traditional number-theoretic assumptions, which maybe vulnerable to attack in the era of quantum-computers. Besides, the effective revocation of user attributes is also crucial in searchable encryption. To overcome these challenges, this paper proposes a new multi-authority ciphertext-policy attribute-based searchable encryption scheme for securely sharing encrypted data in the cloud. By calling Shamir’s threshold secret-sharing technology twice, we achieve co-management of the master key by attribute authorities and interactive generation of user private keys. Furthermore, the KUNodes algorithm is employed for attribute revocation, offering a mechanism to update private keys for non-revoked users. Compared to other schemes, MCP-ABSE-AR introduces multiple attribute authorities responsible for managing user attributes collectively. Additionally, it provides functionalities for keyword searching and attribute revocation. Finally, the proposed scheme is proved to be semantically secure under the decision learning with errors problem in the standard model.

Measurement error in longitudinal earnings data: evidence from Germany

We present evidence on the extent of measurement error in German longitudinal earnings data. Qualitatively, we confirm the main result of the international literature: longitudinal earnings data are relatively reliable in a cross section but much less so in first differences. Quantitatively, in the cross section our findings are very similar to those of Bound and Krueger (J Labor Econ 9:1–24, 1991) and Pischke (J Bus Econ Stat 13:305–314, 1995) for the United States while we find even stronger evidence that first-differencing exacerbates measurement error problems. We also show that measurement error in our survey data is not “classical” as it is negatively correlated with administrative earnings and positively autocorrelated over an extended period of time. Additionally, we estimate a model of measurement error stemming from underreporting of transitory earnings shocks in combination with a white-noise component and make a number of methodological contributions. Our results are robust to the use of two different linked survey-administrative data sets and various other sensitivity checks.

Error Approximation of the Second Order Hyperbolic Differential Equationby Using DG Finite Element Method

This article presents a simple efficient and asynchronously correcting a posteriori error approximation for discontinuous finite element solutions of the second-order hyperbolic partial differential problems on triangular meshes. This study considersthe basis functions for error spaces corresponding to some finite element spaces. The discretization error of each triangle is estimated by solving the local error problem. It also shows global super convergence for discontinuous solution on triangular lattice. In this article, the triangular elements are classify into three types: (i) elements with one inflow and two outflow edges are of type I, (ii) elements with two inflows and one outflow edges are of type II and (iii) elements with one inflow edge, one outflow edge, and one edge parallel to the characteristics are of type III. The article investigated higher-dimension discontinuous Galerkin methods for hyperbolic problems on triangular meshes and also studied the effect of finite element spaces on the superconvergence properties of DG solutions on three types of triangular elements and it showed that the DG solution is <i>O(h<sup>p+2</sup>)</i> superconvergent at Legendre points on the outflow edge on triangles having one outflow edge using three polynomial spaces. A posteriori error estimates are tested on a number of linear and nonlinear problems to show their efficiency and accuracy under lattice refinement for smooth and discontinuous solutions.

Experimental and Engineering Simulation of Loss Characteristics of Silicon Steel Wafers Based on Levenberg-Marquardt Optimization Algorithm

Abstract The loss characteristics of 20WTG1500 silicon steel wafers are simulated and studied by using the Epstein square circle method. First, the loss-measured data are fitted based on the classical Bertotti loss model. When working, a Levenberg-Marquardt (L-M) algorithm is proposed to calculate the parameters of the loss model accurately. For the problem of significant calculation errors of hysteresis loss and stray loss in the classical loss separation model, a variable parameter closely related to the magnetic induction intensity is constructed by using the fixed parameter values of the classical loss model. Then, the loss improvement model is proposed. The accuracy of the improved model is proved by comparing the fitting results of the enhanced model with the measured data.

DIGITAL RECRUITMENT REVOLUTION: EVALUATING THE EFFECTIVENESS OF ONLINE JOB PORTALS

Abstract – The feelings of WEB users have a great benefits for students , colleges and companies. It is necessary to structure well the unstructured data from various placement platforms for proper and meaningful analyses. For the classification of multilingual data, the analysis of feelings has recognized significant information od student and companies. This is called the Job Portal that may be used to maintain the visible connection between company and colleges and companies and student through the mediator Admin. To solve the problem of old job portal this introduced that it has minimum manual work for minimum error and other problems. These architectures may be used Java based languages. In this research variuos data, we had started this project to give more and opportunities for student placement. Key Words: project, job, management, system, application, online software, deployment, security.

Fast high-precision 3D shape measurement using linear phase encoding with geometry constraints

A three-dimensional (3D) profile measurement method based on geometric constraints combined with linear phase encoding (GCPE) is proposed. This method encodes the wrapped phase and the linear signal in the same phase domain, achieves the elimination of the phase ambiguity within the local fringe period, and obtains an unambiguous absolute phase within the entire period through geometric constraints. Compared with the traditional phase encoding method, this method solves the problem of period sequence errors caused by a large number of codewords by encoding linear signals in the phase domain and using a period order correction method to deal with the period jump phenomenon caused by noise. At the same time, the measurement range of the fringe projection system under geometric constraints is significantly improved. Experimentally, 3D profiles of standard planes, complex statues, and separated objects were measured by the use of the GCPE. The results show that the GCPE has the advantage of fast speed and high accuracy in measuring the 3D profile of objects.

Research on Sliding Mode Variable Structure Model Reference Adaptive System Speed Identification of Bearingless Induction Motor

To improve the speed observation accuracy of the bearingless induction motor (BL-IM) and achieve its high-performance speed sensorless control, an improved sliding mode variable structure model reference adaptive system speed identification method based on sigmoid function (sigmoid-VS-MRAS) is proposed. Firstly, to overcome the problem of initial values and cumulative errors in the pure integration link of the reference flux-linkage voltage model, the rotor flux-linkage reference voltage model has been improved by using an equivalent integrator instead of the pure integration link. Then, in order to improve the rapidity and robustness of speed identification, the sliding mode variable structure adaptive law is adopted instead of the PI adaptive law. In addition, in order to optimize the sliding mode variable structure adaptive law and overcome the sliding mode chattering problem, a sigmoid function with smooth continuity characteristics is used instead of the sign function. Finally, on the basis of the inverse system decoupling control of a BL-IM, simulation experiments were conducted to verify the sigmoid-VS-MRAS speed identification method. The research results indicate that when the proposed speed identification method is adopted, not only higher identification accuracy and rapidity can be achieved than traditional PI-MRAS methods, but it can also eliminate the problem of high-frequency vibration (with an amplitude of about 3.0 r/min) when using the sign-VS-MRAS method; meanwhile, the steady-state tracking speed with zero deviation can still be maintained after loading.

An improved IPT-PLL technology for single-phase grid-connected inverters in complex power grid conditions

Aiming at the common problems of frequency variations and harmonics in complex power grids, an improved inverse Park transform phase locked loop (IPT-PLL) technology for single-phase converters suitable for micro grid systems is proposed. Firstly, in the phase detection of PLL, the α component of Park transformation is selected as the reference voltage, and its orthogonal component is constructed using a 1/4 fundamental period delay method. Secondly, Lagrange interpolation polynomials are introduced to approximate fractional delay to solve the problem of delay calculation errors caused by frequency changes. Thirdly, in order to compensate for the poor ability of traditional proportional integral (PI) regulators, multi resonant controllers are superimposed to suppress low order harmonic disturbances. Finally, the design method and system performance of the PI regulator and each resonant controller are analyzed theoretically. The experimental results show that the proposed improved IPT-PLL method has strong adaptability to complex power grids. It can significantly improve the tracking performance of power grid frequency, suppress the interference of low order harmonics and DC bias. And it has good dynamic and static performance.

Intangible Capital in Factor Models

The transition from a traditional manufacturing-based economy to a knowledge- and service-based economy over recent decades resulted in a considerable rise in intangible capital, most of which is not reported on companies’ balance sheets. As a result, balance sheet-based valuation ratios, investment measures, and other firm characteristics that do not incorporate off-balance sheet (OBS) intangible capital suffer from significant measurement error problems. We incorporate a new measure of OBS intangible capital into firm characteristics, such as book to market, investment, and profitability, to address these measurement errors. These OBS intangible adjustments improve the performance of the Fama–French three- and five-factor models and the q-factor model, especially during recent decades. We further find that the value factor is no longer redundant in these empirical factor models. This paper was accepted by Lukas Schmid, finance. Funding: The authors are grateful for financial support from the 2019 EDHEC Scientific Beta “Advanced ESG & Factor Investing” Research Chair. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01261 .

Secure authentication of identity information for quantum resistant IoT devices based on R-LWE and QIBE

ABSTRACT In order to improve the security of Internet of Things device authentication, a security framework is built in this paper by combining the ring-learning band error problem with quantum indistinguishable bilinear encryption. The research method meticulously designs different test scenarios and conducts multiple rounds of testing to evaluate the accuracy of the system authentication. The results indicated that the authentication accuracy was improved to 93.3% on average after using the proposed algorithm. The authentication speed is significantly improved with a shorter operation cycle. After parameter optimization, repeated tests indicated that the accuracy rate remained stable at over 90%, and the accuracy rate reached 100% in specific phases. Combining the ring learning with error problem technique with quantum indistinguishable bilinear encryption greatly improves the authentication security of Internet of Things devices against quantum computing threats. This discovery provides new strategies for Internet of Things security against quantum attacks and has far-reaching theoretical and practical implications for protecting critical infrastructure.

Speed Sensor-less Control of Induction Motor based on Super-helical Sliding Mold

Background: With the continuous progress of technology and the development of shield enterprises, higher requirements are put forward for speed estimation and system stability in the sensorless control system of induction motors in shield machines. Method: In order to solve the problems of velocity observation error and sliding mode vibration of the induction motor velocity observation method based on sliding mold observer, an improved superhelical sliding mold observer is proposed by combining the currently known invention patents and the information based on the induction motor control. Result: The open square of the current error value is introduced into the continuous function of the sliding variable of the observer, and two different functions are used in the boundary layer to replace the sign function, which reduces the sliding mode noise and vibration problems, and the stability of the observer is proved by the Lyapunov stability theory. After that, a phase-locked loop alternative model-referenced adaptive algorithm is used as the rotational speed observation link, which solves the problem that the conventional model-referenced adaptive algorithm relies on the current-modeled magnetic chain observer. Conclusion: The improved super-helical sliding mode observer and phase-locked loop control method effectively reduce the system vibration and speed estimation error of the motor under different speeds and loading conditions, and the observed currents are smoother, which improves the stability of the system. Simulation and experimental results under different working conditions show the effectiveness of the proposed method.

Investigation of IEEE 802.16e LDPC Code Application in PM-DQPSK System

With the development of the Internet and information technology, optical fiber communication systems need to meet people’s information demand for large capacity and high speed. High-order phase modulation and channel multiplexing can improve the capacity and data rate of optical fiber communication systems, but they also bring the problem of bit error. To improve the transmission quality and reliability of optical fiber communication systems, forward error correction (FEC) coding techniques are commonly used, which serve as the fundamental approach to enhance the quality and reliability of fiber optic communication systems, ensuring that the received data remain accurate and reliable. The FEC in optical fiber communication systems is divided into three generations. The first generation FEC is mainly hard decision codewords, represented as RS code. The second generation FEC is mainly cascaded code, which stands for interleaved cascaded code. The third generation of FEC mainly refers to soft decision codes, which are represented as low-density parity-check (LDPC) codes. As a kind of FEC, LDPC codes stand out as pivotal contributors in the field of optical communication and have gained remarkable attention due to exceptional error correction performance and low decoding complexity. Based on IEEE802.16e standard, LDPC code with specific code length and rate is compiled and simulated in MATLAB and VPItransmissionMaker 10.1 and successfully incorporated into polarization multiplexed differential quadrature phase shift keying (PM-DQPSK) coherent optical transmission system. The simulation results indicate that the bit error rate (BER) can be reduced to 10−3 when the optical signal-to-noise ratio (OSNR) reaches 14.2 dB, and the BER experiences a reduction by nearly three orders of magnitude when the OSNR is 17.2 dB. These findings underscore the efficacy of LDPC codes in significantly improving the performance of optical communication systems, particularly in scenarios demanding robust error correction capabilities. This study provides valuable, significant results regarding the potential of LDPC codes for enhancing the reliability of optical transmission in real-world applications.

L-PCM: Localization and Point Cloud Registration-Based Method for Pose Calibration of Mobile Robots

The autonomous navigation of mobile robots contains three parts: map building, global localization, and path planning. Precise pose data directly affect the accuracy of global localization. However, the cumulative error problems of sensors and various estimation strategies cause the pose to have a large gap in data accuracy. To address these problems, this paper proposes a pose calibration method based on localization and point cloud registration, which is called L-PCM. Firstly, the method obtains the odometer and IMU (inertial measurement unit) data through the sensors mounted on the mobile robot and uses the UKF (unscented Kalman filter) algorithm to filter and fuse the odometer data and IMU data to obtain the estimated pose of the mobile robot. Secondly, the AMCL (adaptive Monte Carlo localization) is improved by combining the UKF fusion model of the IMU and odometer to obtain the modified global initial pose of the mobile robot. Finally, PL-ICP (point to line-iterative closest point) point cloud registration is used to calibrate the modified global initial pose to obtain the global pose of the mobile robot. Through simulation experiments, it is verified that the UKF fusion algorithm can reduce the influence of cumulative errors and the improved AMCL algorithm can optimize the pose trajectory. The average value of the position error is about 0.0447 m, and the average value of the angle error is stabilized at about 0.0049 degrees. Meanwhile, it has been verified that the L-PCM is significantly better than the existing AMCL algorithm, with a position error of about 0.01726 m and an average angle error of about 0.00302 degrees, effectively improving the accuracy of the pose.

Inovasi Aplikasi Sikepo dalam Meningkatkan Kinerja Pegawai di Kabupaten Bojonegoro

In the application of the digital attendance system, problems still often occur, an online attendance system that can be misused by the absence of employees at the workplace. The purpose of this research is to find out the discipline of the State Civil Apparatus by innovating new developments about the location-based ASN attendance monitoring system called SiKepo, making it easier for employees to access this system from anywhere. Researchers used qualitative research techniques in this study, using a qualitative approach to the case study method. Based on the results of the study, the innovation of the SiKepo application can improve employee discipline, efficiency, and effectiveness. Because there is data monitoring where employees are required to leave in the morning, with attendance using electronics. And can also use a fingerprint machine that is connected to the application if there are system error problems. After the implementation of SiKepo, there was an increase in the percentage of attendance, which used to be employees who deliberately skipped school with an average of 5 to 6 times a month, now employees are more disciplined. However, frequent error constraints require the uprage system to be better.

Improved YOLOv8-Pose Algorithm for Albacore Tuna (Thunnus alalunga) Fork Length Extraction and Weight Estimation

Aiming at the problems of large statistical error and the poor real-time performance of catch weight in the ocean fishing tuna industry, an algorithm based on improved YOLOv8-Pose for albacore tuna (Thunnus alalunga) fork length extraction and weight estimation is proposed, with reference to the human body’s pose estimation algorithm. Firstly, a lightweight module constructed using a heavy parameterization technique is used to replace the backbone network, and secondly, a weighted bidirectional feature pyramid network BIFPN is utilized. Finally, the upper and lower jaw and tail feature points of the albacore tuna (Thunnus alalunga) were extracted using the key point detection algorithm, and the weight of the albacore tuna (Thunnus alalunga) was estimated based on the fitted relationship between fork length and weight. The experimental results show that the improved YOLOv8-Pose algorithm reduces the number of model parameters by 13.63% and the number of floating-point operations by 14.03% compared with the baseline model without decreasing the accuracy of the target detection and key point detection and improves the model inference speed by 374%. At the same time, it reduces the drift of the key point detection, and the error of the comparison with the actual albacore tuna (Thunnus alalunga) body weight is not more than 10%. The improved key point detection algorithm has high detection accuracy and inference speed, which provides accurate yield data for pelagic fishing and is expected to solve the existing statistical problems and improve the accuracy and real-time performance of data in the fishing industry.

Iris Recognition based on Statistically Bound Spatial Domain Zero Crossing and Neural Networks

Purpose The iris pattern is an important biological feature of the human body. The recognition of an individual based on an iris pattern is gaining more popularity due to the uniqueness of the pattern among the people. Iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. Methods This article discusses a highly error-resistant technique to implement a biometric recognition system based on the iris portion of the human eye. All iris recognition algorithms of the current day face a major problem of localization errors and the enormous time involved in this localization process. Spatial domain zero crossing may be the simplest and least complex method for localization. Yet, it has not been used due to its high sensitivity to erroneous edges, as a consequence of which more complex and time-consuming algorithms have taken its place. Appropriate statistical bounds imposed on this process help this method to be the least erroneous and time-consuming. Errors were reduced to 0.022% using this approach on the CASIA v1 & v2 datasets. Time consumption in this stage was the least compared to other algorithms. At the comparison stage, most algorithms use multiple comparisons to account for translation and rotation errors. This is time-consuming and very resource-hungry. Results The current approach discusses a robust method based on a single comparison, which works with a correct recognition of over 99.78% which is clearly demonstrated by tests. Conclusions The technique is to use a neural network trained to recognize special statistical and regional parameters unique to every person’s iris. The algorithm also gives sufficient attention to consider illumination errors, elliptical pupils, excess eyelash errors and bad contrast.

A novel grey prediction model with four-parameter and its application to forecast natural gas production in China

Due to the non-homology problem and the simple structural characteristics, a grey prediction model will have defects in modeling. In this paper, the structure of the GM(1,1,x(1)) model is deformed, and additional parameters are added. A novel four-parameter grey prediction model NFGM(1,1) is established to avoid the non-homology problem. The accumulation order of the NFGM(1,1) model is optimized to enhance its performance. This paper first introduces a nonlinear term and a linear term into the GM(1,1,x(1)) model to compensate for its structural defects, which can enhance the accuracy of the model in modeling complex modeling sequences. Secondly, a simplified basic formula of the model is proposed to estimate its parameters and iteratively establish the model, which can avoid the problem of non-homologous errors during modeling. Then a novel four-parameter grey prediction model NFGM(1,1) is constructed. Thirdly, the unbiasedness of NFGM(1,1) is proved and verified by matrix theory. Fourthly, by optimizing the order of the NFGM(1,1) model, the model is more flexible and adjustable, and a novel fractional-order four-parameter grey prediction model FNFGM(1,1) can be obtained. Finally, the FNFGM(1,1) model is applied to the prediction of natural gas production in China. The model results show that the FNFGM(1,1) model exhibits superior performance compared to the NFGM(1,1), TWGM(1,1), TDGM(1,1), DGM(1,1), and GM(1,1) models, with the mean relative simulation/prediction/comprehensive percentage errors of 0.92%/1.42%/1.07%, respectively. According to the predicted results, China's natural gas production will reach 3542.9 × 108 m3 in 2027 and some relevant policy recommendations are put forwarded.

A novel method for studying the wind speed probability distribution and estimating the average wind energy density

Traditional distribution models generally have large fitting errors at low wind speeds and poor fitting effects at multi-peak wind speed distributions. In this paper, a novel approach is proposed to fit different wind speed distributions, introducing a Gumbel distribution into common hybrid distribution models. The model parameters are solved by a combination of snake optimizer and nonlinear least squares (SO-NLS), using the optimal values obtained by the nonlinear least squares method as a set of initial input vectors for the snake optimizer. Simulation experiments were conducted using multi-peak wind speed distribution datasets with varying characteristics, comparing the fitting performance of the improved hybrid models against the conventional Weibull, Normal, and Rayleigh hybrid models. The results show that the proposed approach improved the model fit effects, particularly at low wind speeds, in all five experimental datasets. In most cases, the overall fitting effects were also improved. Furthermore, the validity and superiority of the improved hybrid models were further verified by comparing the estimated average wind energy density. Meanwhile, the experimental results also verified that SO-NLS not only yielded better optimization results but also accelerated the convergence speed than the snake optimizer. The improvements presented in this study effectively address the problem of large fitting errors at the low wind speed sections of the distribution, providing a theoretical basis for wind farm planning and design.

Cattle Body Intelligent Measurement based on Improved CenterNet

Traditional cattle body measurement methods have limitations such as manual contact measurement and low efficiency. To improve the efficiency of measuring cattle bodies and reduce labor costs, an improved intelligent measurement network based on CenterNet was proposed. In this study, we used the DenseNet-100 to replace the ResNet-101 to alleviate the vanishing gradient problem. We also improved the cattle body size measurement efficiency by reducing the network parameters. In addition, we conducted a comparative experiment on the cattle posture dataset collected by ourselves to verify the feasibility of the network. The experiment confirmed that the proposed intelligent measurement based on improved CenterNet outperforms the traditional networks and other advanced object measurement networks in accuracy and measurement efficiency, and can effectively solve most of the error problems caused by manual measurement. Besides, our network has good applicability and strong stability, which can meet the requirements of the evaluation of cattle body size measurement indicators.

Research on Full-Field Dynamic Deflection Measurement of Beams Based on Dense Feature Matching and Mismatch Removal Method

To solve the problems of measurement errors led by mismatches of dense feature matching in machine vision structural deflection measurement, this paper proposes a dense feature extraction, matching, and dual-step mismatch-removal-based full-field structural dynamic deflection measurement method. First, the of dense feature detection and matching theory is introduced to extract the SIFT feature points on a structural surface in an image sequence and matched by FLANN to trace the structure movement, and the mechanisms and causes of mismatches are analyzed. Then, a dual-step mismatch removal method combining RANSAC and Structural Displacement Continuity Restriction (SDCR) is introduced to achieve full-field dynamic beam deflection measurement. The proposed method is validated through indoor cantilever beam experiments, and results show that the method can effectively eliminate a large number of SIFT feature mismatches (accounting for approximately 55% of the total matched feature points). The full-field dynamic displacement field of the beam can be measured with the correctly matched dense feature points by converting dense feature point displacements into continuous and uniform spatiotemporal deflections of the structure. A comparison with the GOM Correlate Professional DIC measurement system was conducted, and the maximum measurement error of the cantilever beam dynamic displacement of the proposed method is between 0.024 and 0.053 mm, the root mean squared error of displacement is approximately 0.01 mm, and the correlation coefficient between two deflection–time curves reaches 0.9964. The proposed algorithm is proven to be effective in full-field displacement measurement and has great potential in future structural health monitoring of bridges.