Miracle-dz/UniFAR-FTUs huggingface dataset:
Interturn Fault Severity Monitoring in Symmetrical Six-Phase PMSMs Using Subspace Negative-Sequence High-Frequency Current Residuals

This article proposes a fault severity monitoring method for interturn short-circuit fault (ISCF) based on the xy subspace negative-sequence high-frequency (HF) current residuals in symmetrical six-phase permanent magnet synchronous machines. For six-phase machines, both the fundamental frequency and HF behaviors in the xy subspace can be used to design fault indicators. However, another fault type, high-resistance connection faults (HRCFs) can cause similar xy subspace fundamental current symptoms to ISCF. To avoid incorrectly identifying HRCF as ISCF, the proposed method uses the xy subspace negative-sequence HF current residuals to reduce the sensitivity to the HRCF. Moreover, the proposed method has high robustness for different work conditions and does not need additional sensors. Experimental results show the effectiveness of the proposed method for different fault severities and operations.
Modeling Truncation-Based Approximation Error in Stochastic Computing Circuits

Developments in approximate stochastic computing (ASC) are promising for fast and low-power computing in edge devices based on SoC technology. ASC is anticipated to be useful in machine learning and image processing applications where overall precision of computation can be lowered by truncation of low-order bits in bitstreams to achieve acceptable results just enough to meet the design constraints and performance requirements. In this paper, we propose a quantitative approach to evaluate the approximation error of stochastic computing circuits so the optimal compromise between precision and performance can be achieved.
Multi-channel 600V-level Driver for Piezoelectric-Electrohydrodynamic Hybrid Inkjet Printer

We propose a multi-channel 600V-level electro-hydrodynamic (EHD) driver capable of implementing a drive-per-nozzle (DPN) function to compensate for the deviation of each nozzle. The proposed driver features a serial peripheral interface (SPI) and a serial-to-parallel converter to generate high-voltage pulse width modulation (PWM) signals with different pulse widths and pulse delays for each nozzle to implement the DPN. To achieve uniform ink conditions across the nozzles, these high-voltage pulses can be adjusted for each nozzle and the electric field applied to the nozzles can be controlled. In this way, driving can be realized according to the condition of each nozzle. The proposed and designed EHD driver has been successfully implemented through high voltage technology using Bipolar-CMOS-DMOS process.
Adaptive Tag Comparison for Hybrid Cache

This paper presents a proposal for an STT-RAM (Spin Transfer Torque Magnetic Random Access Memory) and SRAM (Static Random Access Memory) hybrid cache architecture and adaptive tag comparison for this architecture, considering the drawbacks of high-power consumption and the high cost of cache size expansion in conventional SRAM-based cache architectures for performance enhancement. Although the hybrid cache structure allows for an increase in memory size relative to the area, it has the drawback of increased tag comparison. The proposed adaptive tag comparison method involves dynamically changing the flag associated with the memory state and the value of threshold, which determines the memory state based on access count, in real-time to make decisions about tag comparison. By adopting the hybrid cache architecture, an average power reduction of 51.01% is achieved. And applying adaptive tag comparison, further power consumption is reduced by an average of 34.12% without degradation of performance, through an average reduction of 53.14% in tag comparison overhead compared to the hybrid cache scheme.
Unified Theory Acceptance and Use of Technology Against Unplugged Computational Thinking Activities

Occasionally, there may be a low impact on learning engagement between teachers and students due to, for example, poor internet connection or simply because students have lost their focus. One of the methods to improve learning engagement is through a research framework called Unified Theory Acceptance and Use of Technology (UTAUT). This paper adopted 17 UTAUT questions to compare with research variables (pre-post surveys and genders). The user acceptance and use of technology or UTAUT consisted of Likert scale (Strongly disagree to Strongly Agree) questions, computed to generate total scores for each participant. Then, the scores will be compared to each variable to investigate the relationships between these variables against the UTAUT. This study could be implemented via online learning, but in the end, the offline (unplugged) method was the only method to deliver the learning activities. There is limited evidence discussing UTAUT within the area of offline learning; hence, this paper will focus on another spectrum regarding the UTAUT study. Instead of mobile (online) learning engagement, this paper studies the relationship between unplugged learning with user acceptance and the use of technology. Spearman correlation indicated that the unplugged learning approach has a significant positive relationship with user acceptance and use of technology. It revealed that students may also demonstrate an interest in online learning engagement and be able to comprehensively accept the online learning approach. In contrast, Mann-Whitney U test analysis on genders revealed no significant difference. The findings could contribute to another noteworthy discussion regarding the validity of user acceptance and the use of technology against the unplugged learning approach.
Assessing Students' Intentions to Read Online Newspapers Using TAM and TTF Approaches

Public interest in reading social media is higher and is considered more interesting than reading on news platforms because social media is a platform that can build social life both formally and informally so that it can increase their participation in social life more broadly. Technology Acceptance Model (TAM) and Task Technology Fit (TTF) can be used to analyze people's reading interest in online newspapers. The purpose of this research was to provide advice to online newspapers companies to improve the quality of their website or application to increase students' intention to read online newspaper. This study used a sample of 185 students who had the intention of reading online newspapers. This research model uses a structural equation model (SEM) to analysis relationship between independent variable and dependent variable. The result is partially independent variables, such as intrinsic motivation (IM), perceived ease of use (PEU), and technology task fit (TTF) have a positive influence on behavioral intention (BI). R square adjusted of 0.583, meaning that the variables PEU, PU and TTF determine the BI variable by 58.3%.
Fusion of Vibration and Current signals for Improved Multi Class Fault Diagnosis of Three Phase Induction Motor

Immense advancement in Artificial Intelligence and Machine Leaning, data driven condition monitoring and fault diagnosis of three phase Induction Motor (IM) are gaining popularity as an integral part of industrial informatics and automation. Bearing faults are most commonly occurring mechanical defects in three phase induction motor and can easily be detected by motor frame vibration. On the other hand, it has been observed that the electrical faults have direct impact on stator current as compared to mechanical blemishes and they are well detected through the stator current signature analysis. With the scenario of variable speed induction motor drives with power electronics converters, stator winding is susceptible to frequent failures due to adverse impact of various harmonics contents. This paper reports a novel investigation of multi class fault diagnosis of IM with fusion of motor frame vibration and stator current signal to achieve improved classification accuracy both for the electrical and mechanical faults. Random Forest (RF) based machine Learning framework has been used to demonstrate the efficacy of the proposed fusion method.
Self-healing Recovery of Distribution Network Based on Weakness Identification

This paper proposed a weakness identification method in order to find the potential fault equipment in distribution networks. The outage risk model of lines and nodes are created based on power flow and voltage, respectively. Supposing that equipment with high failure risk malfunction, the self-healing recovery model of distribution network is created to obtain the optimal restoration strategy with lowest cost. The restoration strategies consist of network reconfiguration and dispatchment of mobile emergency resources (MERs) and the restoration cost are caused by those strategies. After that, the IEEE 33-bus system is used to testify the effectiveness of the abovementioned models. As shown in the results, the lines and nodes with high outage risk were found by applying the weakness identification method and the outage risk of lines and nodes decreased by applying the restoration strategy.
Optimized Pallet Localization Using RGB-D Camera and Deep Learning Models

This paper presents an optimization and multiobjective evaluation of deep learning (DL) models to improve pallet localization with an RGB-D camera in scenarios of forklift insertion. To this end, we experimentally evaluate three distinct DL models: Detectron2 for combined detection and segmentation, YoloV5 for detection only, and a combination of YoloV5 and UNet for detection and segmentation. Through the automatic hyperparameter optimization process on a custom dataset, 30 configurations were selected, each demonstrating a unique trade-off between precision and speed. Out of these, three Pareto optimal models were chosen for a more detailed analysis, considering inference speed and localization errors along each orthogonal axis. The results suggest that the proposed hybrid model combining YoloV5 and UNet exhibited a promising balance of speed and accuracy, making it suitable for real-time applications. Finally, the proposed model is demonstrated for a fork insertion task in a new environment.
A GPU Ray Tracing Implementation for Triangular Grid Primitives

Triangular grid primitives are a new technique for more efficient handling of memory intensive meshes, also called micro meshes in recent proprietary hardware implementations. This makes it a technique with high potential in the area of virtual environments where hardware capabilities are typically limited. In this poster, we focus on software ray tracing on GPUs and present a novel, easy-to-implement approach that uses a two-level bounding volume hierarchy (BVH) to accelerate these grids. The primary goal of our work is to make the technology more accessible by focusing on standard GPU devices without hardware ray tracing units. With our approach, we are able to encode geometry and BVH with approximately 7.5bytes per triangle, reducing standard representations by a factor of 3.73 while reducing BVH construction time. Our data structure achieves a peak performance impact of 16% for a three-level subdivision.
ARTICULATORY MANIFESTATION OF FOCUS-INDUCED PROMINENCE IN SEOUL KOREAN

Speech units under phrase-level prominence are known to be produced with longer, larger, and faster constriction gestures. Evidence, however, comes mainly from head-prominence languages. The present electromagnetic articulography study investigates the effect of focus-induced prominence on articulatory manifestation in Seoul Korean, an edge-prominence language that marks phrase-level prominence via Accentual Phrases (APs) and also lacks word-level prosody. Special interest is given in examining the scope of the effect as well as its interaction with boundary marking at the level of the Intonational Phrase (IP). Results from seven native Seoul Korean speakers suggest that constriction gestures are longer, larger, and faster under focus. The scope of the effect extends away from the initial syllable of the focused AP reaching up to its third syllable. An interaction between focus and final IP boundary is detected, indicating an intricate relationship between information structure and prosodic structure, discussed within the framework of Articulatory Phonology.
VERTICAL DISTRIBUTIONS OF SOUND VELOCITY IN SEA FLOOR MEASURED IN SOUTH CHINA SEA AND HUANGHAI SEA

This paper presents the vertical distribution of sound velocity in the first few ten meters of the sea floor measured in the South China Sea (12 stations) and the Huanghai Sea (2 stations) by reflection and refraction methods.The apparent characters of the sediment samples and the velocities estimated from the sample porosities by Anderson's empirical equation basically coincide with the measured velocity structures.A new method called "total reflected ray method" has been used to interpret the reflection data.
Transmitter IC Enabling Magnetic Field Shaping for High-Efficiency Wireless Charging of Multiple Receivers

In this paper, we introduced two transmitter (TX) integrated circuits (ICs) which enabled magnetic field shaping for high-efficiency wireless charging. Through the control of the amplitude and phase of current in multiple TX coils, the generated magnetic fields were optimally shaped for the efficiency (ηSYS) maximization. This method was advantageous for the wireless charging of not only a single receiver (RX) but also multiple RXs. Both ICs were fabricated by a 0.18 μm CMOS process with 1.8 V devices and related wireless power transfer (WPT) systems were implemented. Experimental results demonstrated that the proposed ICs enabled improvements in ηSYS and load power in WPT systems whether RX coils were perpendicular to or parallel with TX coils.
Exploring Machine Learning Adoption in Customisable Processor Design

Machine learning has recently been deployed in many areas of processor design with promising results. This paper presents novel capabilities that enable exploration of design flow customisation involving machine learning adoption for customisable processor architectures. We introduce Orbit-ML, an approach that supports customisation of design flow for parametric architecture descriptions to allow experiments with machine learning techniques for optimising processor designs. Orbit-ML facilitates exploring domain-specific design flow with various tools such as design space exploration, source-level transformation, high-level synthesis, and static and dynamic optimisations. Our Orbit-ML prototype illustrates a customised design flow targeting a tunable processor, showing that Bayesian learning can find optimised designs by traversing only 4% of the total design space.
Deep Learning Approaches to Detecting Malicious Domains in DNS: Challenges and Opportunities

The Domain Name System (DNS) operates as the backbone of the internet. However, due to the lack of built-in security features during its initial protocol design, DNS has been exploited for various malicious activities such as spreading malware, initiating Distributed Denial of Service (DDoS) attacks, facilitating fraudulent hosting, and phishing, among others. Given this context, the potent capabilities of deep learning have demonstrated potential in mitigating such pervasive threats. This paper begins by surveying the current state of research in malicious domain detection based on manual feature extraction and deep learning, underscoring the transformative potential of the latter approach. It further identifies and discusses the existing problems and primary challenges within this domain. Considering the distinct feature patterns of single-character concatenated DNS domain names and word concatenated DNS domain names, along with the long-term sequential dependency of domain name sequences and the boundary information among different distributions, we advocate for an in-depth analysis of the data features inherent to DNS domain names. Lastly, we propose research directions for malicious domain detection utilizing deep learning. The proposed detection model is intended to be applicable to both single-character concatenated and word concatenated DNS malicious domain names. Specifically, for word concatenated domain name sequences, we suggest a heightened focus on sensitive events within the domain name sequence to understand the latent patterns of word concatenated malicious domain names, thereby bolstering detection capabilities.
Image Classification on Resource-Constrained Microcontrollers

Recently, as IoT devices have become popular, research to perform deep learning in small devices such as micro-controllers has been attempted. Microcontrollers have very limited resources compared to edge devices such as mobiles. Therefore, in order to perform deep learning-based image classification in a microcontroller, an optimization technique considering HW constraints is required. To this end, in this paper, we present a method for light weighting a model so that it can be executed in a microcontroller, and a process for distributing the lightweight model to a microcontroller. Finally, it was confirmed that image classification can be performed in an actual microcontroller through STM32F746G-Discovery.
Jammo-VRobot: A Cost-Effective Solution for Social Skills Training Programme for Children with High-Functioning Autism

This study reviews the efficacy of incorporating a 3D environment alongside a virtual interactive robot for the purpose of imparting communication skills training to high-functioning autistic children. The research presents a non-immersive virtual setting known as interactive Jammo-VRobot, showcasing a three-dimensional robotic entity. The objective is to elevate the social abilities of HFA children by providing a user-friendly program that can be effortlessly employed by parents or educators in either home or school settings. This program for social skills training is derived from effective methodologies utilised with tangible robots. The conducted study, including 15 participants, focused on three social abilities, emotion identification and expression, imitation, and intransitive gestures. Finding from the experimental sessions suggest promising outcomes, revealing that the interactive Jammo-VRobot environment effectively enhances the particular social skills addressed in the study. Comparisons with physical robots used in state-of-the-art approaches demonstrate similar positive results, highlighting that Jammo-VRobot is equally effective in training the interpersonal abilities of HF autistic children. The results imply that Jammo-VRobot effectively tackles certain constraints linked to the accessibility and expenses associated with interactive robots and immersive virtual environment solutions.
An intelligent medical image diagnosis and treatment architecture based on ChatGPT

In this study, a ChatGPt-based medical image diagnosis and processing architecture were proposed to compensate for the shortcomings of doctors’ subjective misdiagnosis and computer-aided diagnosis in traditional medical image diagnosis. The architecture is divided into an image description generation system and ChatGPT. The former interprets medical images and generates corresponding diagnostic reports, while the latter is responsible for processing diagnostic reports and providing treatment recommendations. In image description generation, we use three text similarity algorithms to evaluate the similarity between the diagnostic report generated by the method and the diagnostic report provided by the doctor and prove that the diagnostic report generated by the algorithm has a high degree of similarity and effectiveness. In the ChatGPT-assisted diagnosis and treatment section, we asked the cardiovascular medical team to score the treatment suggestions generated by ChatGPT. The results showed that the overall acceptance of the suggestions given by ChatGPT was high. Despite the limitations of the data set in this study and the fact that XCA images were only used as an example, the method is expected to be extended to other types of medical images and is expected to become an essential part of intelligent diagnosis and treatment in the future.
NOTES AND CORRESPONDENCE Prediction of East African Seasonal Rainfall Using Simplex Canonical Correlation Analysis

A linear statistical model, canonical correlation analysis (CCA), was driven by the Nelder‐Mead simplex optimization algorithm (called CCA-NMS) to predict the standardized seasonal rainfall totals of East Africa at 3-month lead time using SLP and SST anomaly fields of the Indian and Atlantic Oceans combined together by 24 simplex optimized weights, and then ‘‘reduced’’ by the principal component analysis. Applying the optimized weights to the predictor fields produced better March‐April‐May (MAM) and September‐October‐November (SON) seasonal rain forecasts than a direct application of the same, unweighted predictor fields to CCA at both calibration and validation stages. Northeastern Tanzania and south-central Kenya had the best SON prediction results with both validation correlation and Hanssen‐Kuipers skill scores exceeding 10.3. The MAM season was better predicted in the western parts of East Africa. The CCA correlation maps showed that low SON rainfall in East Africa is associated with cold SSTs off the Somali coast and the Benguela (Angola) coast, and low MAM rainfall is associated with a buildup of low SSTs in the Indian Ocean adjacent to East Africa and the Gulf of Guinea.
Research on 5G Dual Domain Private Network Scheme Based on UPF Diversion Technology

With the evolution of 5G mobile communication technology and network construction, vertical industries such as government, education, culture and tourism, and industry are showing an increasing demand for seamless access to both customer intranets and the internet. This paper studies two types of dual domain private network solutions for 5GToB/ToC, fixed area and wide area roaming based on UPF diversion technology. In order to solve the problems of terminal restrictions and data routing detours, a dual domain private network solution based on dedicated DNN+intelligent diversion UPF is further proposed, which achieves the goal of 5G industry customers not leaving the park with data. The new requirement of “seamless access to office intranet servers and the internet without changing cards, numbers, and perception switching” is proposed based on dedicated network slicing, achieving flexible office work.
Multimodal Emotion Recognition in Video, Audio, and Text using Deep and Transfer Learning

Emotions play an important role in an efficient communication process. Many researchers have studied emotion recognition, however, the majority of their work has concentrated on one modality like text, or numerous modalities like text and speech, but just for one language. This imposes a restriction on the ability to recognize speech, text, or video. Considering multiple modalities at once is the answer to this. There is little study that takes into account all three modalities simultaneously. Therefore, in this paper, we propose a multimodal approach that detects the emotion expressed by a user in a video input where we will not only analyze the facial expression but also take the speech and the text into account while predicting the emotion and expand it to cross-language scenarios. The video input taken detects facial structure and the respective emotion by using some well-known deep learning models. For speech emotion prediction we use three datasets out of which one of them is a different language to implement the cross-language and cross-corpus scenario. The text extracted from audio is given as input to emotion recognition models within the Machine Learning and Deep/Transfer Learning domains. Our results suggest that the multimodal system has a lot of credibility due to the consideration of all 3 modalities and give us a much more accurate context or breakdown of the emotions interpreted from the user input.