Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilizing Consumer-grade Wearable Sensors for Unobtrusive Rehabilitation Outcome Prediction

Rehabilitation outcome prediction can be useful for clinicians providing therapy services to patients undergoing inpatient medical rehabilitation. Machine learning models trained with medical record information available at admission can predict rehabilitation outcomes fairly well. In our previous work, we found rehabilitation outcome prediction accuracy can be improved by also including inertial sensor-based features that objectively quantify patient movement abilities during therapy tasks. In this paper, we extend our prior work by unobtrusively and continuously collecting minute-by-minute movement data from 15 patients throughout their stay of inpatient rehabilitation using inexpensive, consumer-grade fitness trackers, specifically the Fitbit Charge with heart rate. From the Fitbit time series data, we extract features related to physical activity, heart rate, and sleep quality. We use these features as inputs to machine learning models to predict the discharge Functional Independence Measure (FIM) rehabilitation outcome. We also utilize patient similarity techniques to improve prediction accuracy. Results indicate prediction accuracy with the consumer-grade sensor data is close to the same accuracy as prior work using research-grade inertial sensor data. Using consumer-grade fitness devices to obtain highly accurate FIM predictions can help clinicians plan therapy activities during the inpatient stay, as well as assist with discharge to an appropriate setting.
Characteristic Analysis and Prediction of Runoff Based on Chaotic Wavelet Neural Network

This paper proposes a method combining phase space reconstruction and chaotic wavelet neural network to predict runoff. The mutual information method and Cao method are selected to calculate the delay time and embedding dimension, and the Lyapunov exponent is used to judge the chaos. In the chaotic wavelet neural network model, the reconstructed time series is used as the input of the neural network, and the wavelet function is used as the activation function of the neuron. Applying the model to the prediction of the daily runoff and monthly runoff of the Mississippi River, the results show that the model is effective for predicting data of different scales and still has high precision under a long prediction period.
Partial Discharge Online Monitoring and Localization for Critical Air Gaps Among SiC-Based Medium-Voltage Converter Prototype

As a consequence of higher blocking voltage and power density by using SiC devices, detailed analysis and careful design of insulation are important for future medium-voltage power converters. Because of the insulation or mechanical coordination, air gaps inside the converters cannot be avoided. Moreover, some gaps are more critical than others, since much oversized design or electric shielding there cannot be acceptable. Admittedly, the final design should be partial discharge (PD) free under normal operation with a safety margin. However, due to possible change of air properties and insulation degradation, massive PD can be still initiated from high electric field regions along the gaps and may lead to the damage of devices, through the highly coupled power and driving circuitry. Therefore, with online PD monitoring and localization, insulation health condition of the critical gaps can be provided, which could have great values for converter operation, maintenance, and design. In this paper, after detailed acoustic signal propagation study, a fiber-optic acoustic sensor array has been implemented into a 6-kV H-bridge power electronics building block prototype, in order to realize online PD monitoring along the critical gaps. Finally, from experimental results, the proposed sensor array has good sensitivity, localization accuracy, and high immunity to the electromagnetic interference. It unveils a novel access to insulation online monitoring for future high voltage, high power density SiC-device-based converters.
Power Quality Disturbance Detection based on Improved Lifting Wavelet

A new power quality disturbance detection algorithm based on improved lifting wavelet is proposed to solve the problem of power quality disturbance detection in power system. The algorithm combines the weighted average filtering algorithm to optimize the prediction of lifting wavelet decomposition. After the disturbance signal is decomposed by lifting wavelet, the disturbance mutation point is detected by the principle of modulus maximum. The simulation and test waveform analysis of power quality disturbance show that the algorithm has higher accuracy and stronger noise resistance compared with the existing detection algorithm based on lifting wavelet.
A Review of Sentiment Analysis for Non-English Language

Recent researches show extensive progresses in integration of sentiment analysis in the English language. On the other hand, the development of sentiment analysis for non-English languages is still infancy. The article discusses non-English language as an object to review the sentiment analysis process. As a result, this article provides and evaluates information on what process that should be considered, what the recent workshops on sentiment analysis, and what challenges that should be solved in non-English sentiment analysis.
Identification of objects using convolutional neural network by a manipulator for a disaster rescue robot in unstable environment*

We conducted a preliminary study aiming at manipulator autonomously searching for victims in disaster environment. Manipulators need functions such as opening doors, valves, moving debris. Generally, it is necessary for a manipulator to identify these objects using a camera. However, in disaster environments, manipulators equipped with cameras are often unstable. Therefore, we experimented whether objects can be recognized by moving camera. In order to confirm the difference between the fixed camera and the moving camera, we aimed to identify images using an CNN that was as simple as possible, and employed object recognition via image thresholding. We focused on objects related to the door, and experimented on hazard marks and door knobs. If the rescue robot could recognize a hazard mark before entering the room, secondary damage would be prevented. Different door knobs must be turned in different ways. Therefore, in a disaster environment, the rescue robot must identify the type of door knob. The experiment distinguished between various door knobs and the finger pulls needed to open a Japanese Husuma (a type of sliding door). We created a manipulator that can be mounted on a rescue robot to conduct this experiment. This manipulator is equipped with a control system using inverse kinematics. Using this control system, the operator can indicate the target coordinates of the end-effector. In addition, the operator can know the posture of the manipulator in real time, as revealed by simulation.
Chisel: Reshaping Queries to Trim Latency in Key-Value Stores

It is challenging for key-value data stores to trim user (tail) latency of requests as the workloads are observed to have skewed number of key-value pairs and commonly retrieved via multiget operation, i.e., all keys at the same time. In this paper we present Chisel, a novel client side solution to efficiently reshape the query size at the data store by adaptively splitting big requests into chunks to reap the benefits of parallelism and merge small requests into a single query to amortize latency overheads per request. We derive a novel layered queueing model that can quickly and approximately steer the decisions of Chisel. We extensively evaluate Chisel on memcached clusters hosted on a testbed, across a large number of scenarios with different workloads and system configurations. Our evaluation results show that Chisel can overturn the inherent high variability of requests into a judicious operational region, showcasing significant gains for the mean and 95th percentile of user perceived latency, compared to the state-of-art query processing policy.
Study on Identification for the Typical Pasture Based on Image Texture Features

Aiming at the problems of pasture monitoring and low-level digitization in Inner Mongolia desert steppe, the texture feature extraction and image recognition for three typical pastures were represented in this paper so as to provide a basis for grass species identification and grassland management. Images of three typical pasture’s leaves (Herb of Shady Jerusalemsage, Cleistogenes songorica (Roshev.) and Ohwiand potentilla anserine) were preprocessed by using image processing technology, 7 kinds texture features were extracted by using Gray-Gradient Co-occurrence Matrix, and then a BP neural network was built for realizing the image recognition of these three pastures. An 84.7% of final overall recognition rate and an effective classification result of three pasture images were received. The experimental results indicate that the image classification were perfectly realized by using a BP neural network model based on texture features. The recognition for forage image provides a scientific basis for the monitoring of vegetation species diversity, grass degradation and pest control.
Adaptive Output Feedback Control of a Belt System

The main research objective of this article is the vibration abatement of an axially moving system. For achieving it, a suitable adaptive output feedback control scheme is constructed by using observer backstepping technique, high-gain observer and boundary control, where observer backstepping technique is utilized for reconstructing system state and high-gain observer is applied for estimating the unmeasurable state. With the raised control scheme, the uniformly bounded stability can be achieved, and simulation results prove its effect.
Review of Benefit Using Gamification Element for Countryside Tourism

Gamification theory right now has drawn researchers in many areas. This is because gamification has many effects to improve engagement, motivation, learning outcome, entertainment, etc. Gamification in tourism has an effect on tourists, places of tourism and the economy around tourist attractions. In addition, gamification can also be applied to manage workers in tourism places. How gamification can improve tourism attraction to travel to the countryside tour and the benefit of using gamification element will be discussed in this literature review. This literature review collected data about gamification element and its effect from other researchers. Data about gamification of tourism, how that work and what gamification element can support this context will be collected too. The results of this study are that the application of gamification in the tourism sector can be beneficial, ranging from visitors, managers to communities around tourist attractions.
Connotation Analysis and Simulation Research of PQ Decomposition Method

The PQ decomposition method is a classical method for solving the power flow equation. It is widely used in online calculation of power systems because of its simple program, memory saving and fast calculation. The PQ decomposition method (sometimes referred to as the fast decoupling method[1]) is an important calculation method that became widely known as it was introduced in the 1970s, Because it was derived from the Newton-Raphson method and made a series of reasonable simplifications based on the Newton-Raphson method[2]. Therefore, it has a huge advantage in convergence speed and memory usage. Based on the relevant content of Newton's Lawson method, this paper simplifies and corrects it based on it, and finally obtains a simple PQ decomposition method to modify the equation, and conducts analysis and simulation research[3].
A Convenient Kinematic Calibration and Inverse Solution Method for 4-DOF Robot

Calibration can improve the absolute positioning accuracy of the robot end-effector. However, the traditional robot calibration method is complicated, time-consuming and its cost is quite high. Moreover, at such a case, robot cannot convert the full inverse kinematics into known sub-problems. To solve above problems, a convenient method of kinematics calibration and inverse kinematics for 4-DOF robot is proposed. Firstly, the screw axis identification (SAI) method is used to identify the robot twist parameters. Then, the product of exponentials (POE) formula of the robot forward kinematics is established based on the twist parameters. Finally, the closed-form solutions of the 4-DOF robot is implemented using a real algebraic geometric (RAG) method, which is suitable for any RRR type robot. The proposed parameter identification method only needs to separately rotate robot n joints of the robot once. Plus, the corners coordinates is measured by a binocular camera, so that the initial transformation between tool and reference frames and the twist parameters can be easily identified. A non-standard structure 4-DOF robot is used as validation, and the experimental results have proved the convenience, high efficiency, and inexpensiveness of above mentioned method.
Coordinated DMA: Improving the DRAM Access Efficiency for Matrix Multiplication

High performance implementation of matrix multiplication is essential for scientific computing. The memory access procedure is quite possible to be the bottleneck of matrix multiplication. The widely used GotoBLAS GEMM implementation divides the integral matrix into several partitions to be assigned to different cores for parallelization. Traditionally, each core deploys a DMA transfer to access its own partition in the DRAM memory. However, deploying an independent DMA transfer for each core cannot efficiently exploit the inter-core locality. Also, multiple concurrent DMA transfers interfere with each other, further reducing the DRAM access efficiency. We observe that the same row of neighboring partitions is in the same DRAM page, which means that there is significant locality inherent in the address layout. We propose the coordinated DMA to efficiently exploit the locality. It invokes one transfer to serve all cores and moves data in a row-major manner to improve the DRAM access efficiency. Compared with a baseline design, the coordinated DMA improves the bandwidth by 84.8 percent and reduces DRAM energy consumption by 43.1 percent for micro-benchmarks. It achieves higher performance for the GEMM and Linpack benchmark. With much less hardware costs, the coordinated DMA significantly outperforms an out-of-order memory controller.
Compressed Sensing MRI Reconstruction using Low Dimensional Manifold Model

Recent compressed sensing (CS) approaches utilize the similarity and redundancy of image patches of the magnetic resonance (MR) image to enable reconstruction from highly undersampled k-space measurements. In this paper, the patches similarity and redundancy is exploited by assuming that the image patches sample a low-dimensional manifold embedded in a high dimensional space. The MR image is then reconstructed by keeping the dimension of the patch manifold as small as possible. This is achieved by using the dimension of the patch manifold of a MR image as a regularizer in the objective function. Results show that the proposed method is superior to state-of-the-art patch-based MRI reconstruction methods. The algorithm is evaluated on two datasets containing 100 MR images each. The reconstruction quality of the algorithm, gauged using the three quality metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and normalized root mean square error (NRMSE), is better than the comparison methods.
A Reinforcement Learning-Based QAM/PSK Symbol Synchronizer

Machine Learning (ML) based on supervised and unsupervised learning models has been recently applied in the telecommunication field. However, such techniques rely on application-specific large datasets and the performance deteriorates if the statistics of the inference data changes over time. Reinforcement Learning (RL) is a solution to these issues because it is able to adapt its behavior to the changing statistics of the input data. In this work, we propose the design of an RL Agent able to learn the behavior of a Timing Recovery Loop (TRL) through the Q-Learning algorithm. The Agent is compatible with popular PSK and QAM formats. We validated the RL synchronizer by comparing it to the Mueller and Müller TRL in terms of Modulation Error Ratio (MER) in a noisy channel scenario. The results show a good trade-off in terms of MER performance. The RL based synchronizer loses less than 1 dB of MER with respect to the conventional one but it is able to adapt its behavior to different modulation formats without the need of any tuning for the system parameters.
Design of Amazon Chess Evaluation Function Based on Reinforcement Learning

The computer chess is an important research direction of artificial intelligence. Amazon chess game is an extremely complicated strategy game and the search algorithm relies heavily on the evaluation function. The design of evaluation function faces great challenges. This process requires a comprehensive consideration of the characteristics of the board and build up a series of complex formulas, which requires a lot of human experience to support. The author tries to apply the reinforcement learning method to the design process of Game of the Amazons evaluation function. Using the self-game generated data sets and reward signals to stimulates network, it could learn how to evaluate the situation of chess board without human experience. Then this neural network can be used as an evaluation function. Finally, through battling with the game of Amazon chess game using traditional evaluation function, it is verified that the evaluation function design method proposed in this paper can reduce the human experience requirement in the design process of Game of the Amazons evaluation function and effectively evaluate the situation.
Extraction of Maritime Road Networks From Large-Scale AIS Data

Extracting road network information including lane boundaries, lane centerlines, junctions and their relationship from AIS data plays an important role in location based services, urban computing and intelligent transportation systems, etc. However, AIS data are large scale, high noisy, the density and quality are very uneven in different areas, extracting a whole, continuous and smooth maritime road network with rich information from such data is a challenging problem. To address these issues, this paper proposes an adaptive maritime road network extraction approach that can extract both lane boundaries and centerlines for a large sea area from AIS data. Based on a road network definition including nodes, segments and segment curves, the approach designs parallel grid merging and filtering algorithms to determine if a grided area is inside lane or not. Lane boundaries are smoothed through jagged edge filtering and Simple Moving Average algorithms before centerline extraction. We evaluate our method based on real world AIS data in various area across the world’s seas. Experimental results show the advantage of our method beyond the close related work.
Automated Identification of Persistent Time-Domain Features in Seismocardiogram Signals

In the field of cardiac monitoring, the seismocardiogram (SCG) measures the movement of the chest wall using accelerometers and gyroscopes. A key limitation of SCG signals is their sensitivity to transient signal disruptions primarily due to motion artifacts. This work describes a method for automated extraction of time-domain features in SCG signals in the presence of such artifacts, using an iterative method of clustering and re-sampling features to optimize consistency. The accelerometer (axl) and gyroscope (gyr) features extracted with this method are shown to correlate more strongly (median $R^{2}=0.88\ (\mathbf{axl}), 0.88 (\mathbf{gyr})$) with the reference standard for pre-ejection period (PEP), impedance cardiography (ICG), than both peak-counting $(R^{2}=0.29\ (\mathbf{axl}), 0.48\ (\mathbf{gyr}))$ and manual labeling $(R^{2}=0.44\ (\mathbf{axl}), 0.38 (\mathbf{gyr}))$ in the post-exercise period. This result has implications for the feasibility of at-home SCG monitoring.
A New Machine Learning Based Framework to Identify Protein Glycation Sites Using Comprehensive Features and the mRMR Method

Accumulation of the final product during glycation reaction often leads to many diseases, such as diabetes, Alzheimer’s disease and atherosclerosis. Identifying the glycation site can help researchers to understand the pathogenesis and provide new ideas on how to treat these diseases. In this paper, we develop a new predictor by using the support vector machine which apply four feature extractions to encode peptide chains such as binary code sequence, grey incidence degree, accessible surface area and secondary structure probability. The maximum relevancy minimum redundancy (mRMR) feature selection algorithm is used to select the optimal 170 features for the prediction problem. In training set, the performance of Gly-Predict is assessed with an accuracy of 84.815%, a sensitivity of 80.156%, a specificity of 88.868%, and a Matthews’s correlation coefficient (MCC) of 68.411% by k-fold cross validation (k = 5). To objectively evaluate Gly-predict, we tested our model on an independent dataset and compared with previous predictor. The results indicate that Gly-predict is superior to existing glycation site predictors.
A Multi-Gestures Recognition System Based on Less sEMG Sensors

With complex functions, hand is an important organ for human. Unfortunately, many people in China are suffering from hand losing. Therefore, the effective hand motion recognition system is required to help the amputees live or work normally. The surface electromyography (sEMG) signal can represent the hand motion effectively, and many studies about sEMG-based prosthetic hands have been investigated. However, some prosthetic hands use on-off switch control command, which limits the intelligence and flexibility of the prosthetic hands. Some intelligent recognition systems require too many sensors, which is unrealistic for amputees with limited residual muscles. In addition, some algorithms are too complicated, which brings difficulties for practical applications. To solve these problems, we attempted to recognize six commonly used handgestures with two-channel sensors, and the classification performance and calculation time of different algorithms are compared. Finally, we achieved the recognition accuracy of 91.93% by three time domain features and back propagation neural network (BPNN) classifier, which balances the accuracy and computation time. In future work, the proposed method will be applied to real-time prosthetic hands to improve the amputee’s quality of life.
Terrain-adaptive Bionic Landing Gear System Design for Multi-Rotor UAVs

Nowadays the multi-rotor UAVs are used a lot in military and civilian fields and which are playing an increasingly important role. However, the fixed support frames made of metal or composite material acting as landing gears are widely used on the multi-rotor UAVs, which have shortages and can’t be applied to the landing environment with uneven terrain. So a new kind of terrain-adaptive bionic landing gear is proposed in this article with the function of adapting complex terrains and makes the multi-motor UAVs landing on rough terrain, great slope and step floor. The composition and designing principles of the system are introduced at first. Based on the working process, the design method of one-leg system is detailed described as a basic part, and the kinematic solution of multi-leg system is also based on this through coordinate transform. And through virtual simulation, laboratory debugging and flight test, the feasibility and correctness of the design are shown at last. From the jobs in this article, the terrain-adaptive landing gear can be successfully used on multi-rotor UAVs when landing on some complex terrain through adjusting the orientation of the legs. Compared to the traditional design forms, the design has important engineering application value.