Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Biped Robot Walking Behavior: an Analysis of Toe Effect by Using the Gait Generation Method

The study of biped robot has long history and continuation, in order to develop the robot walking process and movement ability like a human. The human walking process is stable and flexible then it can be adapted to various surfaces. In a previous research, the gait pattern design has been studying for improvement of walking control algorithm and a robot foot design etc. For a robot foot design, the research has been focused on a toe component of the biped robot. The biped robot with a toe had shown the good walking ability. From the results, it can conclude that the toe will improve the biped robot walking stability and reduce the load on any joints of the legs. The robot with toe can walk human-like walking. As a reason, I interested in the study of the toe effect emphasized on the bigtoe mechanism. I consider the bigtoe of the foot that is an important component, which distinguishes from the abnormal bigtoe such as bigtoe pain or hallux valgus. It will affect the human walking process. In addition, some medical research papers shown that some body weight are transmitted to the toe during conventional human walking and the bigtoe has a maximum supporting force area. In preceding researches, they have no obvious presentation of the robot with bigtoe. This paper presents a study of biped robot walking process affected by the bigtoe. Furthermore, I will consider an appropriate bigtoe size that make a robot can walk with specified conditions. This research utilizes gait generation method for generating gait pattern of the robot joints. Additionally, the research concentrates on a situation that the robot walks on flat ground and friction is assume to be constant. From the results of our experiments, I obtained a good result from model B (a model composed of bigtoe, tiptoe and heel), which a ratio between bigtoe width and foot width equal 0.28. Moreover, the range of this ratio is in a scope of human bigtoe size. The expected outcome from this research, which results from the effect of the bigtoe, is an application in the robot's foot utilized, to analyze and compare the results. I expect that the bigtoe will has important benefit in the development of biped robot walking ability. “I am a slow walker, but I never walk backwards” -Abraham Lincoln“I can accept failure, but I can’t accept not trying” -Michael Jordan-
Using Deep Learning Techniques for Sandwich Panels with Truss Core Damage Detection

In the previous study, the accuracy of damage detection for the sandwich structures with truss core (SPTC) was affected by the selected damage index, Other than this, human subjective judgment could also not directly determine the degree and the location of damage for SPTC. In this paper, the deep learning method is applied to identify the damage for SPTC, and the dataset of the training deep learning model is obtained based on the dynamic method. This paper adopts to the Caffe, which is a deep learning open source framework, object detection model Faster R-CNN is utilized to study the lattice sandwich plate. The damage data set, the optimal hyperparameters for training the deep learning model, and the optimal ratios of the test set and training set for damage dataset are also studied. It is difficult to detect the damage of SPTC applying to the deep learning algorithms, so the good results cannot be gotten. In this paper, the method of Faster R-CNN has used extracts the deep features of the defective target by ZF that is a kind of Convolutional Neural Network (CNN), the method effectively solves the problem that the traditional algorithm cannot effectively detect the damage. As to the damage of SPTC that the traditional algorithms could also identify, the deep learning algorithm is excelled, the experimental mean average precision(mAP) can be raised to 90%. At the same time, the deep learning method can effectively identify locations and size of the damage in SPTC, the method is proven that the accuracy is higher and the speed is faster for damage detection. In the future, a real-time damage monitoring system is possible, and the theory is worth exploring further.
Wiretapping the Brain: Communication of Spatial Selectivity between Frontal and Parietal Cortices

Our interactions with the world around us are often guided by vision. The ability to perceive a visual stimulus and plan an eye movement to it requires the coordination of many brain areas. Though we have identified many networks of brain areas that work together during these processes, the content of the information exchanged between them is still poorly understood. We sought to answer this question by analyzing the flow of spatial selectivity information in simultaneously recorded neural activity of two cortical areas, the frontal eye fields (FEF) and the lateral intraparietal area (LIP). First, we found that the spatial selectivity of the location of the visual stimulus but not the saccade target is well encoded by the local field potential (LFP) induced power. We determined this by comparing the spatial selectivity of the multi-unit neuronal activity (MUA) to that of the LFP at the same recording site. Next, we explored the interactions between FEF and LIP for insight into the exchange of information between the two areas. We analyzed the low-frequency neural signals across areas and found that there is less coherence between signals around the saccade onset when the saccade is directed to the preferred target location of both recording sites compared to the nonpreferred location. For a more direct analysis of the interactions between the two areas, we analyzed the LFP of one area with the MUA of the other by calculating the spike-field coherence (SFC). We found that a small number of recording site pairs exhibited significant SFC, but that the SFC was not significant across the population of pairs. In summary, we found that the LFP signals both encode the spatial preference of visual stimuli and indicate cortical processing related to saccade generation and execution in FEF and LIP. Both areas appear to conduct this cortical processing largely independent of one another, though they have long been assumed to be working in concert. These results indicate a much more nuanced view of the functional interactions across the frontoparietal network.
A Class-D Amplifier with Digital PWM and Digital Loop-Filter using a Mixed-Signal Feedback Loop

A digital class-D audio amplifier is presented that is based on digital pulse-width modulation (PWM) and a combination of digital and analog feedback. Unlike other recent implementations for low-power applications this amplifier directly accepts digital input as the digital-to-analog conversion is an inherent feature of the presented topology.The feedback topology uses a hybrid scheme with digital feedback to improve the performance of the PWM and analog-feedback to mitigate analog imperfections and to improve power supply rejection. Because of the proposed feedback scheme, the requirements on the analog-to-digital converter (ADC) in the feedback loop are greatly relaxed allowing the use of a continuous-time ∆Σ-modulator with low power consumption.The class-D amplifier was realized in a standard 180 nm CMOS technologies and drives 1.2 W into an 8 Ω load and achieves a total harmonic distortion plus noise (THD+N) of −96 dB, a signal-to-noise ratio (SNR) of 99.9 dB having an efficiency of 91 %.
Action analysis and control strategy for rat robot automatic navigation

A rat robot is an animal robot, where a rat is connected to a machine system via a braincomputer interface. Electrical stimuli can be generated by the machine system and delivered to the rat’s brain to control its behavior. The sensory capacity and flexible motion ability of rat robots highlight their potential advantages over mechanical robots. There are two challenges of rat robot automatic navigation. The first challenge is to recognize the action status of the rat robot, which is an essential feedback for determining the stimuli/instructions for it to accomplish certain movements. The second challenge is the design of the automatic instruction model that steers the rat robot to perform navigation. Due to inherent characteristics and instincts of the rats, the controlling strategy of the rat robots is different from mechanical robots. In this thesis, we propose a new idea for analyzing the action states of the rat robot. A miniature camera is mounted on the back of the rat robot and the egocentric video captured by the camera is used to analyze its action. We propose two action analysis methods. The first method is based on an optical flow algorithm and the second method incorporates deep neural networks. We propose two automatic instruction models. The first model learns from manual control data to mimic the human controlling process, and the second model issues instructions according to human experts’ knowledge. We build a rat robot and apply these models to enable it to navigate in different scenes automatically. In order to produce more accurate optical flow estimation, we propose a row convolutional long short-term memory (RC-LSTM) network to model the spatial dependencies among image pixels. Our RC-LSTM network is integrated with Convolutional Neural Networks and achieves competitive accuracy. To analyze potentially more complex actions from the egocentric videos, we extend our deep neural networks used for rat states analysis to be a two-stream architecture. A spatial attention network is incorporated to help our model to focus on the relevant spatial regions to recognize actions. Our model is evaluated on two egocentric action recognition datasets and achieves competitive performance.
Research and Development of High Performance Finite Element for Large Scale Acoustic Analysis Method

In recent years, numerical methods such as the finite element method are developed for sound field prediction of architectural spaces. However, large-scale analysis is often necessary because of the larger domains and the higher frequencies that we deal with in engineering applications. Therefore, we often only consider larger domains at low frequencies or high frequencies in smaller domains. In this paper, the iterative domain decomposition method (IDDM) is applied to solve transient and steady-state acoustic problems. By applying the IDDM, the effectiveness of the large-scale acoustic analysis can be shown. In addition, the results of introducing high-order elements into the analysis are shown.
IS Mediation of Emergency Management: Adding Prediction to the existing Framework of Activities?

Fire poses a recurring threat to life and property in every society and common traditions of fire-fighting services and fire emergency management systems have developed over centuries. Such systems now comprise a repository of large datasets gathered from years of fire incidents. Motivated by the devastation caused by a recent major fire emergency in their jurisdiction, the M2inder project was initiated by a statebased fire emergency service organisation to develop a modelling tool using this and other datasets to probabilistically predict where and when fires are likely to occur. The contribution of study presents the findings from a participatory action research (PAR) study of the M2inder project when a team of both practitioners and researchers with varying expertise, was assembled to guide the development of the M2inder system.
Using Conditional Adversarial Networks to Deblur the Sonar Image of the Unknown Motion Blur Kernels

In order to recover the blurred sonar image collected by the side scan sonar during motion, we propose a solution based on the conditional adversarial networks to deblur the sonar image of the unknown motion blur kernels. First, we use improved conditional adversarial networks to recover the sonar image, and improve the loss function, so that the quality of image generation is improved while the training stability is enhanced. Then we propose a method for generating blurred sonar images. The blurred sonar image generated by this method is closer to the real blurred sonar image. Finally, we made our own sonar image set and trained it with two-timescale update rule. The final results proved that the image restored by this method has higher definition.
Ensemble Classifier untuk Klasifikasi Kanker Payudara

Kanker payudara merupakan jenis kanker yang paling banyak diderita oleh kaum wanita di Indonesia. Penyakit tersebut dapat berakibat pada kematian jika terlambat ditangani. Oleh karena itu, deteksi dini kanker payudara merupakan langkah awal untuk menyelamatkan nyawa pasien. Pada penelitian ini telah dilakukan klasifikasi kanker payudara berdasarkan data anthopometric serta data dari hasil tes darah rutin menggunakan single classifier (ELM, SVM dan kNN) dan ensemble classifier yang menggabungkan ketiga algoritma tersebut dengan penentuan kelas majority voting. Pembagian data dilakukan dengan three way data split. Hasil eksperimen menunjukkan bahwa saat menggunakan keseluruhan fitur penggunaan ensemble classifier lebih baik daripada single classifier dalam hal akurasi maupun G-mean. Namun, saat menggunakan 4 fitur terbaik (resistin, glucose, age, dan BMI) penggunaan ensemble classifier sedikit lebih baik dalam hal G-mean. Hal ini disebabkan minimnya diversity di antara classifier sehingga saat digabungkan tidak mampu memperbaiki hasil.
Developing Effective STEM Animations: Application of a Multimedia Learning Theoretical Framework

Although most people believe animations can be very effective for STEM instruction and engagement, research often leads to findings that they are not superior to static graphics or other information presentations. The reason for these failures is not inherent in animations. Rather, the failures often reflect non-adherence to principles of good graphics design and a lack of understanding of principles of STEM learning. Sci-Toons, a series of animations dealing with diverse STEM topics, were developed based on a theoretical learning framework and the use of teams comprised of individuals with scientific expertise and individuals with visual design expertise. The process employed in the development of these animations is presented along with data on the wide-spread dissemination of Sci-Toons, its impacts on viewers, and its impacts on students involved in their production.
Corpus Linguistic Tools for Historical Semantics in Arabic

In this paper, we present a set of corpus linguistic tools for conducting historical semantic research in the Arabic language. We compiled a Historical Arabic Corpus (HAC) that spans more than 1500 years of continuous language use. With techniques from the field of Natural Language Processing (NLP), the tools we presented here have been used to create the HAC and to explore lexical semantic change. The development of these tools is aimed at offering a catalyst to the ambitions goal of compiling an Arabic dictionary on historical principles. HAC and the tools can also be used for conducting research in a variety of areas of linguistics.
Optimal Placement and Sizing of Distributed Generation Sources in Distribution Networks Using SPEA Algorithm

Distribution grids are being transformed from passive to active networks by integrating distributed generation (DG). In this paper, a Strength Pareto Evolutionary Algorithm (SPEA) has been used to estimate the best type of DGs and their optimal capacity and location in a 33-node radial distribution network. In order to improve the voltage stability, expressed by its index, and to reduce the active and reactive power losses, multiple scenarios have been proposed and compared. The backward/forward sweep method of distribution load flow was used to evaluate the three objective functions. The same distribution network has been also used to show the effect of the capacity and location of the DGs on the objective functions.
Speeding up Collective Communications Through Inter-GPU Re-Routing

In order to address the vast needs of disparate domains, computing engines are becoming more sophisticated and complex. A typical high-performance computational engine is composed of several accelerator units, in most cases GPUs, plus one or more CPU controllers. All these components are becoming increasingly interconnected to satisfy bandwidth and latency tolerance demands from modern workloads. Due to these constraints, solutions to efficiently interconnect them or to systematically manage their traffic—such as PCIe v3, NVLink v1 and v2 on the hardware side, and NVIDIA Collective Communication Library (NCCL) and AMD ROCM layer on the software side—are becoming more commonplace inside HPC systems and cloud data centers. However, as the number of accelerators increases, workloads (especially machine learning) might not be able to fully exploit the computational substrate due to inefficient use of hardware interconnects. Such scenarios can lead to performance bottlenecks where high-bandwidth links are not used by the underlying libraries and under-performing links are overused. This work proposes Workload Optimization Through Inter-GPU Re-routing (WOTIR), which consists of enhanced NCCL-based collective primitives that aim to boost bandwidth utilization (through more efficient routing) and reduce communication overhead. WOTIR targets GPUs with no direct NVLink communication path (which leads to PCIe communications) and instead re-routes communication through intermediate GPUs to bridge NVLink segments and avoid PCIe communications. Such method allows the maximum possible utilization of the NVLink bandwidth between the GPUs without routing through the PCIe bus. Using this method, we see a reduction of up to 34 percent in execution time for selected machine learning workloads when non-optimal GPU allocations arise.
4-Peg Hanoi Towers Algorithm Animation Demonstration System Based on Html5

Differing from the previous studies on demonstration Hanoi Tower algorithm based on desktop implementation, this paper adopts cross-platform HTML5 technology, drew on the references of three-peg Hanoi tower animation on the basis of fully study from the theory and realization of four-peg Hanoi tower, designed the animation implementation of four-peg Hanoi tower, simplified animation implementation and application popularized, intuitively expresses the relationship between the optimal number of moving steps and the section score in four-peg. The implementation method can also be applied to animation demonstration of other algorithm principles, and has a high reference value.
An improved fault detection crow search algorithm for wireless sensor network

In wireless sensor networks (WSNs), the collected data during monitoring environment can have some faulty data, and these faults can lead to the failure of a system. These faults may occur due to many factors such as environmental interference, low battery, and sensors aging etc. We need an efficient fault detection technique for preventing the failures of a WSN or an IoT system. To address this major issue, we have proposed a new nature‐inspired approach for fault detection for WSNs called improved fault detection crow search algorithm (IFDCSA). IFDCSA is an improved version of the original crow search algorithm (CSA). The proposed algorithm first injects the faults into the datasets, and then the faults are classified using improved CSA and machine learning classifiers. The proposed work has been evaluated on the three real‐world datasets, ie, Intel lab data, multihop labeled data, and SensorScope data, and predicts the faults with an average accuracy of 99.94%. The results of the proposed algorithm have been compared with the three different machine learning classifiers (random forest, k‐nearest neighbors, and decision trees) and Zidi model. The proposed algorithm outperforms the other classifiers/models, thus generating higher accuracy and lower features without degrading the performance of the system.
Reinforcement learning integrated with simulation for job-shop scheduling system

A dynamic and real-time system integrating reinforcement learning with simulation is designed for job-shop scheduling.Several stochastic variables are introduced to transform the job-shop scheduling problem into sequential decision problem.The model environment of job-shop scheduling is built by simulation for obtaining the system performance indices and ensuring the feasibility of the solution.Then,a multi-agent Q-learning algorithm integrated with simulation is developed to solve the job-shop problem.Finally,simulation and optimization experiments show the effectiveness of the system.
University Students Using SWISS to Display Images Searched by Keywords Extracted from Utterances

In this paper, a system, Search Websites’ Images using Search Suggestions (SWISS), was improved based on the results of an experiment in which university students discussed their research theme using SWISS. SWISS displayed images based on the extracted words from the students’ utterances and another word predicted from a letter of the alphabet or Hiragana via an autocomplete function. The experiment showed that some entertaining ideas were generated from the students who used SWISS in their brainstorming. On the other hand, SWISS did not operate when no one was speaking, even when the students wanted to see some images to expand their ideas. Therefore, SWISS was improved so that the associated images are displayed even without word inputs to SWISS.
A survey of 3D model retrieval based on shape features

A large number of 3D models are shared in 3D applications,such as CAD product design,3D product exhibition,movie and animation production.But the existing search engines cannot provide an effective 3D model retrieval service for those applications yet.The content-based 3D model retrieval technique is mainly based on the intrinsic shape of a 3D model,such as geometry and topology,so it can be used to search 3D models automatically.This paper summarizes the key techniques of 3D model retrieval based shape features,and collects the commonly used features for retrieval,especially their extraction methods.Methods based on statistical features are easy to implement,but these features cannot describe the mesh shape adequately.Methods based on extended features are good for a model with gentle structure change,but computational cost is high.Voxelization based methods use volume characteristics of the model to speed up the retrieval process,but fail to support multi-resolution search.Methods based on ray sampling are suitable for a simple model,but it is prone to ambiguity.View based methods mainly extract features from two-dimensional projection view of a model and has low computational complexity,but may lose some important 3D structure information.This paper also enumerates and analyzes typical 3D model retrieval systems having been achieved and published up to now.Finally,we conclude this paper with an outlook for the future development of 3D model retrieval.