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This paper presents the implementation and experimental evaluation of a short-packet physical-layer network coding (PNC) system. Implementation of short-packet PNC systems is challenging. First, short packets may have only a few pilot symbols for synchronization and channel estimation purposes. Increasing the number of pilots increases the overhead; decreasing the number of pilots, on the other hand, degrades the packet error rate performance. Second, many short-packet systems are meant for applications with very stringent delay requirements. Employing advanced but complex PNC channel decoding may result in unacceptable delay due to the processing delay. This work presents a low-complexity and low-overhead physical-layer design of OFDM-based short-packet PNC systems, implemented over the software-defined radio platform. Our design makes use of only a small number of pilots (without separate OFDM preamble symbols) to address issues such as slot synchronization, packet detection, carrier frequency offsets, and mismatched channel state information. Our design employs reduced-complexity XOR channel decoding based code-aided parameter estimation (that includes synchronization and channel estimation) to compensate for the limitations imposed by having a small number of pilots. This is the first demonstration that provides a practical framework for applying PNC to short-packet communications.

This study is aimed to find out the types of errors made by the presenters of thesis proposal seminar based on Communicative Effect Taxonomy and to find out whether or not global errors of Communicative Effect Taxonomy made by the presenters of thesis proposal seminar significantly hinder communication between the presenters and the audiences. This study used qualitative approach since the data of this study was taken from 4 students’ presentations which were in the form of words rather than number and statistic. This study revealed that there were 188 errors committed by the students in which 179 and 9 erroneous utterances fall under local error and global error respectively. The local errors consist of 45 lexical errors, 93 morphological errors, and 41 syntactical errors. The global errors consist of 2 wrong order of major constituents, 1 missing, wrong, or misplaced sentence connectors, 1 missing, wrong, or misplaced sentence connectors, and 5 uncategorized global errors. As for the effect of global error to the audience, the writer found out that the global errors did not significantly hinder communication to the audience. Keywords: Error Analysis, thesis proposal seminar, Communicative Effect Taxonomy

A method that combines temperature field detection, adaptive FCM (Fuzzy c-means) clustering algorithm and RBF (Radial basis function network) neural network model is proposed. This method is used to analyze the thermal error of the spindle reference point of the tauren EDM (Electro-discharge machining) machine tool. The thermal imager is used to obtain the temperature field distribution of the machine tool while the machine tool simulates actual operating conditions. Based on this, the arrangement of temperature measurement points is determined, and the temperature data of the corresponding measurement points are got by temperature sensors. In actual engineering, too many temperature measurement points can cause problems such as too high cost, too much wiring. And normal processing can be affected. In order to establish that the thermal error prediction model of the machine tool spindle reference point can meet the actual engineering needs, the adaptive FCM clustering algorithm is used to optimize the temperature measurement points. While collecting the temperatures of the optimized temperature measurement points, the displacement sensors are used to detect the thermal deformation data in X, Y, Z directions of the spindle reference position. Based on the test data, the RBF neural network thermal errors prediction model of the machine tool spindle reference point is established. Then, the test results are used to verify the accuracy of the thermal errors analysis model. The research method in this paper provides a system solution for thermal error analysis of the tauren EDM machine tool. And this builds a foundation for real-time compensation of the machine tool’s thermal errors.

Grammar is complexity in learning English it makes many students often commit errors in learning. Making errors in writing is one of the processes for learning English .The aim of this research was to describe the students’ errors and its causes in using auxiliary verbs in the sentences. In this research, the writer limited the discussion on the error made by the students in using auxiliary verbs. The design of this research was quantitative approach in a form of descriptive analysis (percentage) that included observation, collecting the data the researcher used test as the instrument. The writer conducted the research to 31 students in first semester of English Literature in academic 2021/2022. The data were analyzed by using error analysis method. The error types were classified based on linguistic category, especially surface strategy taxonomy. The writer classified the types of error into omission, addition, misformation, and misordering. Among those four types, the most frequency error was misformation that reached 102 errors or 61.44% . the other finding was the sources of error which are divided into three categories. Those are interlingual errors, intralingual errors, and communicative strategies. Communicative strategies errors is the most common source of error, there were 45.78%

This paper develops the conceptual design and error analysis of a cable-driven parallel robot (CDPR). The earlier error analysis of CDPRs generally regarded the cable around the pulley as a center point and neglected the radius of the pulleys. In this paper, the conceptual design of a CDPR with pulleys on its base platform is performed, and an error mapping model considering the influence of radius of the pulleys for the CDPR is established through kinematics analysis and a full matrix complete differential method. Monte Carlo simulation is adopted to deal with the sensitivity analysis, which can directly describe the contribution of each error component to the total orientation error of the CDPR by virtue of the error modeling. The results show that the sensitivity coefficients of pulleys’ geometric errors and geometric errors of the cables are relatively larger, which confirms that the cable length errors and pulleys’ geometric errors should be given higher priority in design and processing.

The Common Weakness Enumeration (CWE) community publishes an aggregate metric to calculate the ‘Most Dangerous Software Errors.’ However, the used equation highly biases frequency over exploitability and impact. We provide a metric to mitigate this bias and discuss the most significant software weaknesses over the last ten years.

The loss function is a criterion to evaluate the learning quality of a deep convolutional neural network, which represents the gap between prediction and ground truth. However, as the most commonly used loss function in image classification tasks, Cross‐Entropy loss does not encourage the model to distinguish the similarity between features. In this work, the authors investigate inter‐class separability of similar features learnt by convolutional networks and propose a loss function called Error Refactor Loss (ER‐Loss). ER‐Loss is based on the error caused by convolutional networks; it can improve the inter‐class separability and is simple to implement and can easily replace the Cross‐Entropy loss. Compared with softmax loss, ER‐Loss adds a dynamic penalty item which can help ER‐Loss monitor the actual situation of model training and adjust the value of the penalty item according to model training. The ER‐Loss on CIFAR100 and part of ImageNet ILSVRC 2012 is evaluated and the experimental result showed that the ER‐Loss can improve the accuracy of the model.

Machine learning teaches computers to think in a similar way to how humans do. An ML models work by exploring data and identifying patterns with minimal human intervention. A supervised ML model learns by mapping an input to an output based on labeled examples of input-output (X, y) pairs. Moreover, an unsupervised ML model works by discovering patterns and information that was previously undetected from unlabelled data. As an ML project is an extensively iterative process, there is always a need to change the ML code/model and datasets. However, when an ML model achieves 70-75% of accuracy, then the code or algorithm most probably works fine. Nevertheless, in many cases, e.g., medical or spam detection models, 75% accuracy is too low to deploy in production. A medical model used in susceptible tasks such as detecting certain diseases must have an accuracy label of 98-99%. Furthermore, that is a big challenge to achieve. In that scenario, we may have a good working model, so a model-centric approach may not help much achieve the desired accuracy threshold. However, improving the dataset will improve the overall performance of the model. Improving the dataset does not always require bringing more and more data into the dataset. Improving the quality of the data by establishing a reasonable baseline level of performance, labeler consistency, error analysis, and performance auditing will thoroughly improve the model's accuracy. This review paper focuses on the data-centric approach to improve the performance of a production machine learning model.

This paper reports an error analysis of orthographic representation in written Chinese characters among Mandarin as foreign language (MFL) learners studying at an elementary level at a Malaysian public university in their dictation assessment. A total of 262 stroke error types of their orthographic representation in written Chinese characters were collected and analysed. The errors were consequently classified into four main categories among 165 MFL learners who took part in the study. The study found that participants made most mistakes in the stroke numbers and shape of orthographic representation in written Chinese characters. It was also found that there were detectable mistakes in stroke relation and stroke direction of orthographic representation in written Chinese characters. The cognitive factors contributing to the orthographic representation error types in written Chinese characters are discussed. It is concluded that beginner MFL learners would have a greater tendency to commit several character errors in writing Mandarin because of their low level of orthographic awareness and presumably a high cognitive load given to them as they transit from writing alphabets scripts to writing Chinese characters. Future research could examine how MFL learners cognitively adapt when transitioning from alphabet scripts to Chinese characters. Findings would guide instructors in the teaching Chinese characters more efficient and subsequently, it would allow them to interpret orthographic representations and write Chinese characters more accurately.

Traffic flow prediction is a basic aspect to be considered in transportation management and modeling. Attaining precise information on near and current traffic flows has an extensive range of appli...

The existing studies on profile error analysis and machining accuracy measurement do not consider the impact of the theoretical errors on the machine tool accuracy measurement. Therefore, this study proposes an estimation method of the surface profile error based on the normal section plane, using the theoretical flank milled surface for comparison. This effectively improves the accuracy of profile error estimation. The theoretical flank milled surface is the surface machined by flank milling under ideal conditions. Hence, compared to the traditional analysis method based on the designed three-dimensional model of S-shaped test pieces, the calculated profile error of this method does not include theoretical errors, thereby eliminating the impact of theoretical errors on machine tool accuracy measurement and evaluation. First, an improved method for continuous parameterized dual spline interpolation was proposed. It simplifies the solution of the singular problem of the coefficient matrix of the spline basis function and obtains a continuous ideal machining tool axis trajectory surface with complete geometric characteristics. Next, a method for constructing the theoretical flank milled surface machined with a cylindrical milling tool using equidistant mapping characteristics was proposed; then, the differential transformation relationship at the cutting contact point of the curved surface under the influence of tool path errors was established. Furthermore, the normal section plane method based on the differentiation of the cutting contact point was proposed. The problem of solving the distance from a point to a surface is converted to the problem of solving the distance from a point to a curve in the normal section plane. This improves the accuracy of profile error estimation. The effectiveness of the method was verified by comparing the analysis results of the profile errors of a typical cylindrical surface with the point to surface and the point to curve methods.

The realization of the desired damping characteristics based on magnetorheological (MR) dampers is important for semi-active control and useful for the matching process of suspension damper. To reduce the cost of the control system and improve the output accuracy of the desired damping force, this study proposes an open-loop control method featuring an accurate inverse model of the MR damper and a tripolar current driver. The reversible sigmoid model is used to accurately and quickly calculate the desired current. Furthermore, the change characteristic of the desired current is analyzed qualitatively and quantitatively, which shows that the desired current needs to change suddenly to make the actual damping force velocity curve quickly approach the desired one. To meet the demand of the desired current, a tripolar current driver controlled by an improved PI control algorithm is proposed, which is with fast response and low noise. Finally, the bench test verifies that the control system can achieve different desired damping characteristics well, and the inherent error in this process is explained through the gap between the available damping force area and the desired damping characteristic curve and the crossover phenomenon of the dynamic characteristic curves of the MR damper.

The reliability issues bring great challenges in the performance maintenance of computation-in-memory (CIM), especially based on large-scale resistive random access memory (RRAM) arrays. In this article, we directly characterize the retention of output differential/accumulation current for analog RRAM-based CIM applications. Different from the conventional concerns on the device-level conductance time-dependent fluctuation, this work focuses on the influence of crossbar-level weighted-sum currents on the accuracy loss over time in the general convolutional and fully connected (FC) networks. This is the first Mb-level long-term retention characterization and evaluation in analog RRAM arrays. Comparing with the simulation accuracy based on the short-term device-level test, the computing accuracy values based on crossbar-level characterizations are improved for about 16.8% and 31.3% at 500 and 1000 min at 125 °C and match well with the measured accuracy, indicating that the crossbar-level retention evaluation is more accurate. This work provides new insights for developing RRAM-based CIM systems with excellent reliability.

Modern synthetic aperture radar (SAR) systems for Earth observation from space employ innovative hardware concepts. The key idea is to digitize the output of a multielement antenna almost immediately after the receiver and to dynamically process these data either onboard the radar satellite in real time or on the ground. This article addresses the performance of such digital beamforming (DBF) systems in the presence of phase and magnitude errors in the digital channels. For this, analytic expressions for the sensitivity and range ambiguity performance are derived. These equations are kept general so that they are valid for both planar array antennas and array-fed reflector antennas. It is an important objective of this article to compare these two antenna types to each other. A major conclusion from this analysis is that direct-radiating phased arrays are inherently more susceptible to random phase and magnitude errors compared with array-fed reflector antenna-based systems. This manifests itself in a more rapid degradation of the imaging performance with phased array antennas.

Purpose For decades, continuous research work is going on to maximize the power harvested from the sun; however, there is only a limited analysis on exploiting the microwatt output power from indoor lightings. Microelectronic system has power demand in the µW range, and therefore, indoor photovoltaics would be appropriate for micro-energy harvesting appliances. “Energy harvesting is defined as the transfer process by which energy source is acquired from the ambient energy, stored in energy storage element and powered to the target systems”. The theory of energy harvesting is: gathering energy from surroundings and offering technological solutions such as solar energy harvesting, wind energy collection and vibration energy harvesting. “The solar cell or photovoltaic cell (PV), is a device that converts light into electric current using the photoelectric effect”. Factors such as light source, temperature, circuit connection, light intensity, angle and height can manipulate the functions of PV cells. Among these, the most noticeable factor is the light intensity that has a major impact on the operations of solar panels. Design/methodology/approach This paper aims to design an enhanced prediction model on illuminance or irradiance by an optimized artificial neural network (ANN). The input attributes or the features considered here are temperatures, maxim, TSL, VI, short circuit current, open-circuit voltage, maximum power point (MPP) voltage, MPP current and MPP power, respectively. To enhance the performance of the prediction model, the weights of ANN are optimally tuned by a new self-improved brain storm optimization (SI-BSO) model. Findings The superiority of the implemented work is compared and proved over the conventional models in terms of error analysis and prediction analysis. Accordingly, the presented approach was analysed and its superiority was proved over other conventional schemes such as ANN, ANN-Levenberg–Marquardt (LM), adaptive-network-based fuzzy inference system (ANFIS) and brainstorm optimization (BSO). In addition, analysis was held with respect to error measures such as mean absolute relative error (MARE), mean square root error (MSRE), mean absolute error and mean absolute percentage error. Moreover, prediction analysis was also performed that revealed the betterment of the presented model. More particularly, the proposed ANN + SI-BSO model has attained minimal error for all measures when compared to the existing schemes. More particularly, on considering the MARE, the adopted model for data set 1 was 23.61%, 48.12%, 79.39% and 90.86% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Similarly, on considering data set 2, the MSRE of the implemented model was 99.87%, 70.69%, 99.57% and 94.74% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Thus, the enhancement of the presented ANN + SI-BSO scheme has been validated effectively. Originality/value This work has established an improved illuminance/irradiance prediction model using the optimization concept. Here, the attributes, namely, temperature, maxim, TSL, VI, Isc, Voc, Vmpp, Impp and Pmpp were given as input to ANN, in which the weights were chosen optimally. For the optimal selection of weights, a novel ANN + SI-BSO model was established, which was an improved version of the BSO model.

It has been shown that the coherent detection of long range (LoRa) signals only provides marginal gains of around 0.7 dB on the additive white Gaussian noise (AWGN) channel. However, ALOHA-based massive Internet-of-Things systems, including LoRa, often operate in the interference-limited regime. Therefore, in this work, we examine the performance of the LoRa modulation with coherent detection in the presence of interference from another LoRa user with the same spreading factor. We derive rigorous symbol- and frame error rate (FER) expressions as well as bounds and approximations for evaluating the error rates. The error rates predicted by these approximations are compared against error rates found by Monte Carlo simulations and shown to be very accurate. We also compare the performance of LoRa with coherent and noncoherent receivers and we show that the coherent detection of LoRa is significantly more beneficial in interference scenarios than in the presence of only AWGN. For example, we show that coherent detection leads to a 2.5-dB gain over the standard noncoherent detection for a signal-to-interference ratio (SIR) of 3 dB and up to a 10-dB gain for an SIR of 0 dB. Moreover, we show that with coherent detection it is easier to obtain useful and relevant FER values even for negative SIR values.

Flash-based SSD RAID arrays are increasingly being deployed in data centers. Compared with HDD arrays, SSD arrays drastically enhance I/O performance and density, and reduce power, cooling, and rack space. Nevertheless, SSDs suffer aging issues. Especially, an SSD has limited endurance and needs to be replaced when it reaches to the end of its lifetime. Although prior studies have been conducted to address this disadvantage, effective techniques of RAID/SSD controllers are urgently needed to extend the lifetime of SSD arrays. In this article, we propose a novel RAID architecture, called FreeRAID, to leverage the interplay of RAID and SSD controllers to optimize the lifespan of flash-based SSD arrays. FreeRAID adds a new exploitable phase to the life cycle of flash blocks. In FreeRAID, flash space is separated into normal space and exploitable space, and they are used to serve normal data and approximate data, respectively. We design a dual-space management scheme for RAID controllers to intelligently allocate SSD spaces based on their aging status. Inside an SSD, we propose an adaptive flash translation layer for the SSD controller to maintain the reliability and space efficiency of flash memories. We implemented a prototype of FreeRAID based on an SSD array simulator. Our experiments show that FreeRAID can significantly increase the lifetime by up to $3.07\times $ compared with conventional SSD-based RAID arrays.

Various features presented in the newspaper will add information to the reader. With increasing insight every day, they will increasingly be critical of the conditions that occur around found in writing in the newspaper. The language errors that occur are indicative of a lack of understanding of Bahasa. The researcher used a qualitative research approach with a type of descriptive study to be more focused and in accordance with the research objectives. The subject of this research is Warta Kota daily newspaper from October-December 2018 with the object of research being a mistake in various features of Warta Kota daily newspaper. Based on the data, errors analysis based on the largest to the smallest were Morphological errors (34.91%); Syntax error (16.04%); Error of Absorption Element (12.26); Word Writing errors (11.32%); Phonological errors (8.49%); Punctuation Writing Error (6.60%); Particle / Word Selection Writing errors and Word Combined Writing errors (3.77%); Front Word Writing errors (1.89%); and Error in the Use of Letters (0.94%).Keyword: Error Analysis, Newspaper

This research aims to determine the types of grammatical errors that students make the most and least frequently, as well as the causes of those errors, using the Surface Strategy Taxonomy that was identified in students' recount text translation. The participants in this study were SMAN 1 Pringsewu first-graders in the academic year 2020/2021. This research adopted a descriptive qualitative method by using a translation task and questionnaire for the instruments. According to the Surface Strategy Taxonomy, the students made all types of errors: omission, addition, misformation, and misordering. In recount text translation based on Surface Strategy Taxonomy, the most common error made by students was misformation, whereas the least frequent error type was misordering. Problems with the target language, mother-tongue interference, a lack of eagerness to learn the target language, a lack of confidence, and the absence of error feedback from the teacher were all possible causes of those errors. This indicates that the students’ English grammar understanding was still limited. Therefore, the English teacher is expected to solve it by providing appropriate teaching methods and materials to reduce errors.

This research aims to ascertain the proportion of mistakes made by students while solving comparative and social arithmetic problems. This study falls under the category of qualitative descriptive research. Twenty-six students from class VII.6 were used as study participants. Data were gathered via interviews and administering exams. The analytical framework was derived from Polya's classification of errors. The findings of this research show that type of mistake. According to this research, mistakes in evaluating responses (fourth phase) are more prevalent than other types of errors. The fourth phase received a score of 43,8%, whereas the first phase received a score of 34,9%, the second phase received a score of 35,4%, and the third phase received a score of 36,5%. The mistakes in the first and second phases are indicative of fundamental and conceptual problems. The third level demonstrates mistakes in the processes and algorithms, followed by faults in confirming the solutions. The consequence of this study is the identification of pictures of mistakes made while answering comparison questions and social arithmetic, allowing for the development of more effective learning methods for future learning.

The key job here in the presented work is to investigate the performance of Generalized Ant Colony Optimizer (GACO) model in order to evolve the shape of three dimensional free-form Non Uniform Rational B-Spline (NURBS) curve using stereo (two) views. GACO model is a blend of two well known meta-heuristic optimization algorithms known as Simple Ant Colony and Global Ant Colony Optimization algorithms. Basically, the work talks about the solution of NURBS-fitting based reconstruction process. Therefore, GACO model is used to optimize the NURBS parameters (control points and weights) by minimizing the weighted least-square errors between the data points and the fitted NURBS curve. The algorithm is applied by first assuming some pre-fixed values of NURBS parameters. The experiments clearly show that the optimization procedure is a better option in a case where good initial locations of parameters are selected. A detailed experimental analysis is given in support of our algorithm. The implemented error analysis shows that the proposed methodology perform better as compared to the conventional methods.

An Empirical-Markovian approach is proposed to estimate the pavement structural capacity as a function of key stochastic and design parameters. The key stochastic parameters are the initial and terminal deterioration transition probabilities typically estimated from pavement distress records. These two transition probabilities have a major impact on the pavement performance trend predicted using Markovian processes. In addition, typical pavement design factors are included as related to traffic loadings, materials properties, and climate conditions. In particular, two distinct Empirical-Markovian models are developed to estimate the pavement structural capacity in terms of relative strength indicators such as the structural number (SN) and gravel equivalent (GE). The first model can estimate the structural capacity based on the initial transition probability and relevant design parameters, while the second one deploys the terminal transition probability along with other design parameters. The recommendation is to use the higher of the two structural capacity values estimated for a particular pavement project. The sample models presented based on the California Department of Transportation (Caltrans) design method have resulted in good model fittings as demonstrated by the various deployed statistics and error analysis, thus indicating the usefulness of the proposed Empirical-Markovian approach in estimating the pavement structural capacity for rehabilitation and design purposes. The model exponents can be obtained from solving a linear system of equations using data from a small project sample or solving a multi-variable linear regression model when a large road sample is available. The latter case provided sample generalized models with statistics indicating their high significance.

The dynamic characteristics of the XY worktable are thoroughly analyzed in this study to accurately compensate the measurement errors of on-machine measurement systems caused by the dynamic and static characteristics under multiple factors. The dynamic positioning error model of the XY worktable motion components is established, and the theoretical model of the dynamic positioning error of the XY worktable is derived. The actual parameters of the experimental platform for the correlation error of CNC machine tools are substituted into the model for theoretical calculation and analysis. Results show that the motion speed of the worktable, the weight of the measured workpiece, the installation position of the workpiece, and the friction force are the main factors causing the dynamic positioning error. Dynamic positioning error analysis and verification experiments were conducted to verify the validity of the proposed theoretical model. Experimental results show that a regular change occurs between the dynamic positioning error and the movement speed of the XY worktable, and the actual measurement results are basically consistent with the theoretical calculation results. The research results can lay a good foundation for establishing the measurement error prediction and compensation model of on-machine measurement systems of CNC machine tools and improving its measurement accuracy.

To improve the accuracy of ship track prediction, a fractional-order gradient descent method is adopted into a recurrent neural network (RNN). The convergence of the proposed algorithm is proved. Identification of ship maneuvering behavior, atmospheric information, and oceanographic information is considered in vessel tack prediction. The ship track of Xiamen Port is predicted using the new algorithm. Error analysis is made with different factional orders and traffic busy degrees. Results show that the testing and training error differs with different fractional orders. The predicted track results can not only improve the efficiency of marine traffic management but also prevent and warn the safety accidents, so as to avoid accidents.

This paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions and assign temporal anchors indicating when a possession relation holds between the possessor and possessee. We work with intra-sentential possessor and possessees that satisfy lexical and syntactic constraints. We experiment with traditional classifiers and neural networks to automate the task. In addition, we analyze the factors that help to determine possession existence and possession type and common errors made by the best performing classifiers. Experimental results show that determining possession existence relies on the entire sentence, whereas determining possession type primarily relies on the verb, possessor and possessee.

White modules of quick response codes (QR codes) are degraded by dot gain, especially when printed at smaller sizes, which results in more false blacks than false whites in error analysis, and also severely affects decoding ability. To reduce the false blacks generated from printing and to improve code readability, modifying the microstructure of graphic QR codes by reducing the size of the information dots is proposed. Experimental results show that the resultant QR code reduces the number of false blacks and thus the error rate. This method applies to the physical output of both conventional and graphic QR codes.

Penelitian deskriptif kualitatif ini bertujuan untuk mendeskripsikan hasil analisis kesalahan siswa SMK dalam menyelesaikan soal matematika materi sistem persamaan linier tiga variabel. Penelitian ini dilakukan di SMK Persatuan 1 Tulangan pada tahun ajaran 2019/2020 di kelas X-Tata Busana. Subjek dipilih berdasarkan banyaknya kesalahan yang dilakukan dalam menyelesaikan soal. Adapun teknik pengumpulan data dalam penelitian ini menggunakan metode tes dan wawancara. Tes dilakukan kepada semua siswa di kelas X-Tata Busana. Dari hasil tes soal diambil subjek yang memiliki kesalahan terbanyak. Selanjutnya subjek yang terpilih diwawancara. Hasil tes dan wawancara dianalisis dengan menggunakan analisis kesalahan Newman yaitu kesalahan membaca, kesalahan memahami, kesalahan mentransformasi, kesalahan dalam keterampilan proses dan kesalahan pada jawaban akhir. Berdasarkan penelitian dengan menggunakan analisis Newman dapat disimpulkan bahwa siswa melakukan kesalahan membaca dengan bentuk kesalahan tidak dapat membaca kata-kata penting yang ada dalam soal, siswa melakukan kesalahan memahami dengan bentuk kesalahan tidak dapat menuliskan yang diketahui dan ditanyakan dan menuliskan tetapi salah, siswa melakukan kesalahan mentransformasi dengan bentuk kesalahan tidak dapat membuat model matematika, siswa melakukan kesalahan keterampilan proses dengan bentuk kesalahan tidak dapat menyelesaikan soal dan prosedur tidak sesuai, dan siswa melakukan kesalahan pada jawaban akhir dengan bentuk kesalahan menuliskan jawaban salah dan tidak menuliskan jawaban.

Aiming at the problem that the base coordinate system of industrial robot is not unified with the center coordinate system of tool, and it is impossible to input the action position in the robot teaching device, this paper adopts ROMER HEXAGON METROLOGY 7530SE three-coordinate measuring instrument as the robot measuring equipment and proposes a multi-objective normalization algorithm. In this paper, the reliability of the multi-objective normalization algorithm is verified by the experiment, and the measurement accuracy difference of the multi-objective normalization algorithm under different reference systems is studied. The results have shown that the accuracy of the system reaches 0.8 mm and the root mean square (RMS) value is maintained at 0.3-0.7 mm when the engine is taken as the reference coordinate system within the robot’s 0-50mm motion stroke. When the body-in-white is used as the reference coordinate system, the accuracy of the system can reach 2.2 mm, the RMS value can be maintained at 1.0-1.8mm. The reasons for good accuracy and stability of the measurement system established with the engine as the reference coordinate are analyzed. The multi-objective normalization algorithm proposed in this paper has high engineering universality, and the accuracy analysis of the algorithm under different reference system has guiding significance for the selection of reference coordinate system of measurement system.

Characteristics of the European Centre for Medium-Range Weather Forecast’s (ECMWF’s) 0000 UTC diagnosed 2-m temperatures (T2m) from 4D-Var and global ensemble forecasts initial conditions were examined in 2018 over the contiguous United States at 1/2° grid spacing. These were compared against independently generated, upscaled high-resolution T2m analyses that were created with a somewhat novel data assimilation methodology, an extension of classical optimal interpolation (OI) to surface data analysis. The analysis used a high-resolution, spatially detailed climatological background and was statistically unbiased. Differences of the ECMWF 4D-Var T2m initial states from the upscaled OI reference were decomposed into a systematic component and a residual component. The systematic component was determined by applying a temporal smoothing to the time series of differences between the ECMWF T2m analyses and the OI analyses. Systematic errors at 0000 UTC were commonly 1 K or more and larger in the mountainous western United States, with the ECMWF analyses cooler than the reference. The residual error is regarded as random in character and should be statistically consistent with the spread of the ensemble of initial conditions after inclusion of OI analysis uncertainty. This analysis uncertainty was large in the western United States, complicating interpretation. There were some areas suggestive of an overspread initial ensemble, with others underspread. Assimilation of more observations in the reference OI analysis would reduce analysis uncertainty, facilitating more conclusive determination of initial-condition ensemble spread characteristics.

The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms (PAD) into such systems. Companies and institutions need to know whether such integration would make the system more "secure" and whether the technology available is ready, and, if so, at what operational working conditions. Despite significant improvements, especially by adopting deep learning approaches to fingerprint PAD, current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modeling the cause-effect relationships when two non-zero error-free systems work together. Accordingly, this paper explores the fusion of PAD into verification systems by proposing a novel investigation instrument: a performance simulator based on the probabilistic modeling of the relationships among the Receiver Operating Characteristics (ROC) of the two individual systems when PAD and verification stages are implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms' ROCs submitted to the most recent editions of LivDet (2017-2019), the state-of-the-art NIST Bozorth3, and the top-level Veryfinger 12 matchers. Reported experiments explore significant scenarios to get the conditions under which fingerprint matching with embedded PAD can improve, rather than degrade, the overall personal verification performance.

With the development of smart substations and the promotion of 61850 standards, sampling values based on IEC61850-9-2 have become an important part of smart substation construction. With the popularization and application of the sample value (SV), the interpolation algorithm has been increasingly used in protection, measurement, control, wave recorder and power quality applications. However, the error in the interpolation algorithm poses a challenge to its use. This paper describes the basic methods of linear Lagrange and parabolic Lagrange interpolation and presents maximum theoretical values for the interpolation error when Lagrange linear and second-order approximations of sinusoidal signals are performed. The single-point error of each sampling point is analyzed using the remainder equation, and the harmonic error of the Fourier transform after interpolation is strictly mathematically derived. Finally, the accuracy of the theory is verified by real measurement data, and suggestions for the application of the interpolation method are introduced.

The efficiency and accuracy for parallel robot play a significant role in the industrial production process. In order to further improve the motion performance of parallel robot, this paper focuses on the study of mechanism design, dynamic modeling and error analysis of a cable-linkage serial-parallel palletizing robot (CSPR). Firstly, the CSPR is designed as a series-parallel hybrid mechanism driven by flexible cables, which can effectively reduce the inertia and improve the dynamic response. As is known that kinematic design optimization of compliant mechanisms requires accurate yet efficient mathematical models, the kinematics and dynamic models are established by the homogeneous coordinate transformation method and Lagrange equation. Based on the dynamic mathematical model of the robot, a variety of sensors are chosen to construct the hardware control system and the sliding mode variable structure control strategy is also designed based on motion errors. Then, the motion performance and load carrying capacity of the CSPR were analyzed and compared in different operating conditions respectively, and the results verify the validity and efficiency of the mechanism.

In this work, a comparison between three broadband methods used to estimate the propagation constant of planar transmission lines is presented. The goal of this comparison is to study how possible random measurement errors can affect the use of the aforementioned methods commonly used, since in ideal conditions the same solution is obtained from all of them. For this purpose, a sensitivity analysis is carried out in order to study the similarities and differences and how errors in measured Sparameters and in line lengths affect the attenuation and the phase constant obtained from each method. Subsequently, a minimization approach that consists of a least-square estimation using a criteria to choose the optimal line lengths is proposed to minimize measurement errors. Finally, an experiment has been designed, manufactured using microstrip transmission lines, and measured to validate the developed theory. Results corroborate the proposed theory and show an excellent agreement with electromagnetic simulations in the 0.1- to 50-GHz frequency band, therefore assessing the suitability of the proposed error analysis. This work was supported in part by the Spanish Ministerio de Economía, Industria y Competitividad, under Project ADDMATE TEC2016-76070-C3-3-R (AEI/FEDER, UE), in part by the Spanish Ministerio de Educación, Cultura y Deporte, under Grant FPU16/00246, and in part by Universidad de Málaga, under I Plan Propio de Investigación y Transferencia (C.1).

The paper considers the results of an analytical study of the errors in various scalar calibration algorithms for 3D sensors of a vector physical measurand. Practical recommendations for the implementation of the calibration algorithm are given. The results are confirmed by mathematical simulation.

The automatic recognition of scanned Medieval manuscripts written in square notation still represents a challenge due to degradation, non-standard layouts, or notations. We propose to apply CNN/LST...