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Most of the linear encoders are based on optics. The accuracy and reliability of these encoders are greatly reduced in polluted and noisy environments. Moreover, these encoders have a complex structure and large sensor volume and are thus not suited to small application scenarios and do not have universality. This paper aims to present a new absolute magnetic linear encoder, which has a simple structure, small size and wide application range.

The effect of swing error is analyzed for the sensor structural arrangement. A double-threshold interval algorithm is then proposed to synthesize multiple interval electrical angles into absolute angles and convert them into actual displacement distances.

The final linear encoder measurement range is 15.57 mm, and the resolution reaches ± 2 µm. The effectiveness of the algorithm is demonstrated experimentally.

The linear encoder has good robustness, and high measurement accuracy, which is suitable for industrial production. The linear encoder has been mass-produced and used in an electric power-assisted braking system.

The purpose of this paper is to highlight the increasing use of rotary or angle encoders and examine two distinct types of non‐contact encoders that employ novel technologies to meet the demands of very different applications.

Firstly, examines the design characteristics of optical angle encoders which are used for precision applications such as rotary format computer‐to‐plate pre‐press machines, machine tool A, B and C axes, and surface mount machines. Then considers magnetic encoder design, including the latest OnAxis™ technology for lower cost applications which require less precision, but are often more physically demanding.

Different rotary motion applications demand different combinations of performance and features to optimise their function – some require accuracy, others repeatability, high‐resolution or low‐cyclic error for velocity loop control, plus cost can also be a key consideration.

There is an increasing need to control rotary motion. An appreciation of the design principles of optical angle and rotary magnetic encoders is necessary to ensure that the correct choice of encoder is made for each application. For an angle encoder system, trade‐offs should be made to determine a realistic specification, and whilst many factors can limit achievable accuracy, techniques are available for reducing any shortfall. For applications where cost constraints and lower accuracy specifications demand a magnetic encoder, robust OnAxis™ sensor technology is gaining acceptance amongst designers, and whilst many of the basic design principles of optical encoders still apply, other specific technical aspects, such as much lower resolution and accuracy must be understood before using these devices. Hidden design costs should also be understood such as installation timings and the environmental suitability of magnets.

Will aid designers of rotary motion systems to make a well‐informed selection of encoder type, based on the detailed needs of their applications, including accuracy and cost budgets.

The purpose of this paper is to present analysis of the feedback predictive encoder-based analog-to-digital converter (ADC).

The use of feedback predictive encoder-based ADCs presents an alternative to the traditional two-stage pipeline ADC by replacing the input estimate producing first stage of the pipeline with a predictive loop that also produces an estimate of the input signal.

The overload condition for feedback predictive encoder ADCs is dependent on input signal amplitude and frequency, system gain and filter order. The limitation on the practical usable filter order is set by limit cycle oscillation. A boundary condition is defined for determination of maximum usable filter order. In a practical implementation of the predictive encoder ADC, the time allocated to the key functions of the gain stage and loop quantizer leads to optimization of the power consumption.

A practical switched capacitor implementation of the predictive encoder-based ADC is proposed. The power consumption of key circuit blocks is investigated.

This paper presents a methodology to optimize the bandwidth of predictive encoder ADCs. The overload and stability conditions may be used to determine the maximum input signal bandwidth for a given loop quantizer. Optimization of power consumption based on the allocation of time between the gain stage and the successive approximation register ADC operation is investigated. The lower bound of power consumption for this architecture is estimated.

As a type of angular displacement sensor, the Hall-effect magnetic encoder incorporates many advantages. While compared with the photoelectric encoder, the magnetic encoder nevertheless has lower precision and lower resolution. So, the purpose of this essay is to find a way to increase the precision and resolution of the magnetic encoder.

By combining a single-pole magnetic encoder and a multi-pole AlNiCo magnet, the precision and resolution of this combined magnetic encoder are increased without increasing its volume or complicating its structure. A special algorithm system is developed to ensure faithful encoding and decoding.

Tests show that the combined magnetic encoder, with a diameter of 67.12 mm (including shaft) and thickness of 6.9 mm, has a precision of ±6′, compared with a 15-bit photoelectric encoder and a static resolution of ±0.6′.

This new kind of magnetic encoder could be used in specialized fields which need high-precision servo-control systems that are small, have ultra-low-speed and high-speed ratios and are non-oil-polluting or shock-resistant.

To eliminate the angle deviation of magnetic encoder, this paper aims to propose a compensation method based on permanent magnet synchronous motor (PMSM) sensorless control. The paper also describes the experiments performed to verify the validity of this proposed method.

The proposed method uses PMSM sensorless control method to get high precision virtual angle value, and then get the deviation value between virtual position and magnetic angle which is used as compensation table. Oversampling linear interpolation tabulation method has been proposed to eliminate the noise signals. Finally, a magnetic encoder with precision (repeatability) 0.09° and unidirectional motion precision 0.03 is realized. The control system with an encoder running at 14,000 and 0.01 r/min showing high motion resolution is also realized.

Higher value of current in PMSM leads to a magnetic encoder with higher precision. When using oversampling linear interpolation to tabulate the compensation table, it is understood that more oversampling does not lead to a better result. Finally, validated by experiments, using eight intervals to calculate the mean value of angle deviation leads to the best result.

The angle deviation compensation method proposed in this paper has a great practical implication and a good commercial application. The method proposed in this paper could be effectively used to self-correct the magnetic encoder using arctangent method and also correct any rotary encoder sensor.

This paper originally proposes an adaptive correction method for a rotary encoder based on PMSM sensorless control. To eliminate the noise signals in an angle compensation table, over-sampling linear interpolation tabulation method has been proposed which also guarantees the precision of the compensation table.

In recent years, principal component analysis (PCA) has attracted great attention in dimension reduction. However, since a very large transformation matrix must be used for reconstructing the original data, PCA has not been successfully applied to image compression. To solve this problem, this paper aims to propose a new technique called k‐PCA.

Actually, k‐PCA is a combination of vector quantization (VQ) and PCA. The basic idea is to divide the problem space into k clusters using VQ, and then find a PCA encoder for each cluster. The point is that if the k‐PCA encoder is obtained using data containing enough information, it can be used as a semi‐universal encoder to compress all images in a given domain.

Although a k‐PCA encoder is more complex than a single PCA encoder, the compression ratio can be much higher because the transformation matrices can be excluded from the encoded data. The performance of the k‐PCA encoder can be improved further through learning. For this purpose, this paper‐proposes an extended LBG algorithm.

The effectiveness of the k‐PCA is demonstrated through experiments with several well‐known test images.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

In this investigation, a linear encoder system based on the ultrasonic transducer has been proposed. Ultrasonic transducers are usually designed for distance measurements, such as the time of flight method and sonar system. These applications are defined as discrete-length measurement technologies. The purpose of this study is to develop a continuous displacement measurement system using ultrasonic transducers.

A modified signal processing based on heterodyne signaling is implemented in this system. In the proposed signal processing, there is an automatic gain control module, a phase-shifting module, a phase detection module, an interpolation module and especially a frequency multiplication module, which can enhance the resolution and reduce the interpolation error simultaneously.

The proposed system can generate the encoding signals and is compatible with most motion control systems. For the experimental result, the maximum measurement error and standard deviation are about −0.027 and 0.048 mm, respectively. It shows that the proposed encoder system has the potential for displacement measurement tasks.

This study reveals an ultrasonic linear encoder that is capable of generating an incremental encoding signal, accompanied by a corresponding signal processing methodology. In contrast to the conventional heterodyne signal processing approach, the proposed multiplication method effectively reduces the interpolation error that arises because of multiple reflections.

Intelligent pneumatic actuator (IPA) is a new generation of actuator developed for Research and Development (R&D) purposes in the academic and industrial fields. The purpose of this paper is to show the application of optical encoder and pressure sensor in IPA, to develop a real-time model similar to the existing devices, and to assess the position control performance using a proportional-integrative (PI) controller and a bang-bang controller in real-time.

A micro optical encoder chip is used to detect cylinder rod position by reading constructed laser stripes on a guide rod, whereas a pressure sensor is used to detect the chamber pressure reading. To control the cylinder movements by manipulating pulse-width modulation (PWM) cycles, two unit valves of two ports and two positions were used. A PI controller and a bang-bang controller are used with suitable gain value to drive the valve using PWM to achieve the target actuator position.

The results show the experimental results of the closed-loop position tracking performance of the system using a data acquisition (DAQ) card over MATLAB software.

This paper presents a real-time model used to replace the microcontroller-based system from previous IPA design. The paper proposes two control strategies, PI and bang-bang, to control position using encoder and pressure reading.

The purpose of this paper is to ensure the stable operation of satellites in orbit and to assist ground personnel in continuously monitoring the satellite telemetry data and finding anomalies in advance, which can improve the reliability of satellite operation and prevent catastrophic losses.

This paper proposes a deep auto-encoder (DAE) satellite anomaly advance warning framework for satellite telemetry data. Firstly, this study performs grey correlation analysis, extracts important feature attributes to construct feature vectors and builds the variational auto-encoder with bidirectional long short-term memory generative adversarial network discriminator (VAE/BLGAN). Then, the Mahalanobis distance is used to measure the reconstruction score of input and output. According to the periodic characteristic of satellite operation, a dynamic threshold method based on periodic time window is proposed. Satellite health monitoring and advance warning are achieved using reconstruction scores and dynamic thresholds.

Experiment results indicate DAE methods can probe that satellite telemetry data appear abnormal, trigger a warning before the anomaly occurring and thus allow enough time for troubleshooting. This paper further verifies that the proposed VAE/BLGAN model has stronger data learning ability than other two auto-encoder models and is sensitive to satellite monitoring data.

This paper provides a DAE framework to apply in the field of satellite health monitoring and anomaly advance warning. To the best of the authors’ knowledge, this is the first paper to combine DAE methods with satellite anomaly detection, which can promote the application of artificial intelligence in spacecraft health monitoring.