Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultra-Short-Term Prediction of Wind Power Based on Sample Similarity Analysis

Increasing the utilization rate of wind energy is of great significance to the improvement of energy structure, which is inseparable from the support of wind power forecasting (WPF) technology. However, it is well known that there is no certain WPF model suitable for all conditions, such as different regions or seasons. Therefore, instead of focusing on the combination of machine learning models in a specific scenario, this article proposes a two-stage modeling strategy of “first classify and separately model, then perform pattern recognition” from the perspective of sample similarity analysis. That is, in offline mode, the historical database is divided into multiple categories with different characteristics, and prediction models are established for each category respectively; in online mode, pattern recognition is carried out on the prediction sample to select the corresponding prediction model. In this way, the WPF problem is decomposed into two strongly related tasks: wind power mode classification and wind power numerical prediction. Furthermore, the coupling and connection between mode classification task and numerical prediction task are strengthened through the transfer learning of sample features. Around the above ideas, specific methods of how to classify, identify, and predict are proposed, which are two-level clustering, Convolutional Neural Network (CNN) classification model and Long Short-term Memory (LSTM) prediction models. Simulation results based on real-world datasets prove the effectiveness and superiority of the proposed hybrid model.
Design and Performance Analysis of THz Wireless Communication Systems for Chip-to-Chip and Personal Area Networks Applications

Terahertz (THz) communication is a promising technique for chip-to-chip communication and wireless personal area networks. In this paper, we present an experimental study and design to realize such THz communication systems. We develop two different chip sets for on-off-keying (OOK) modulation based THz transceivers which include carrier generators, modulators, THz amplifiers, and baseband amplifiers. Specifically, the first chip set integrates the circuit blocks for the OOK modulation without the THz amplifier for short-range communication. In addition, the second chip set design includes the THz amplifier modules to extend the coverage of transmission. For these two chip sets, we experimentally demonstrate the feasibility of the wireless communication at THz frequency bands and assess performance using the bit error rate (BER) analysis. We estimate the BER by calculating the signal-to-noise ratio (SNR) based on the eye diagram and compare with actual BER measurements and Monte Carlo simulations. We also address the impact of the distance, the transmit power, and the data rate for the proposed THz transceivers based on the link budget analysis, and confirm the accuracy of the derived BER expression.
Multi-label Disease Classification of Patient's Tweets using Automatically Acquired Training Corpus

Morbidity determination using tweet data is currently drawing huge attention within the field of influenza outbreak prediction. This work aims to develop a multi-label disease classification system targeting real tweets. One of the difficulties associated with developing such a system is how to collect a large amount of training data. In this paper, we propose a method to acquire the training data for the multi-label disease classifier automatically from real tweets. Our experimental evaluation revealed that the proposed method can be applied to construct the multi-label disease classifier successfully. We also investigate the method of using both the automatically acquired data and the manually annotated data together to further improve the classification performance.
Biologically Inspired Vision and Touch Sensing to Optimize 3D Object Representation and Recognition

3D representation and recognition of objects are two pivotal steps for autonomous robots to safely explore and interact with an unknown environment and manipulate objects. 3D modeling can be beneficial in different robotic applications such as object grasping, pose estimation, robot navigation and localization. Real-time data acquisition and accurate object representation are essential in the context of such practical applications. On the other hand, the recognition of the objects in an environment is indispensable for situational awareness and for enabling the robot to interact effectively with complex environments.
Recent Trends and Opportunities of Remote Multi- Parameter PMS using IoT

Healthcare is an aspect of a person’s life which precedes all else. Only when a person is of sound health, can they strive to achieve his/her goals in life. Not all people have equal access to the best healthcare due to financial issues, no accessibility, or other reasons. The aim of this paper is to provide a review of the recent advances of a new low-cost and effective remote Multi-Parameter Patient Monitoring System (PMS) and Remote Patient Monitoring (RPM) systems. These systems are equipped at the patients’ home and can monitor the patients’ condition continuously and alert the relevant authority when the patient needs attention. These systems are necessary to evaluate the response of the disease to treatment and to identify adverse drug reactions. These systems allow their users to leave the hospital early and be monitored and checked on from their home. With the review, the authors discuss some issues available in most systems. The paper also includes some directions for future research. Alternatively, with emerging field of IoT, remote patient monitoring to fundamentally change healthcare services.
Helmet Usage Detection on Motorcyclist Using Deep Residual Learning

In Indonesia and other developing countries, motorcycles are popular means of transportation. With the growing number of motorcyclists, it becomes harder for law enforcement to monitor motorcyclists who do not use a helmet. In this research, we propose a new system to detect motorcyclist that does not wear a helmet from a dashboard camera footage. We use a convolutional neural network to detect motorcyclists from the footage and two different residual network types to count the number of passengers and number of helmets, respectively. Our best motorcyclist detection model can achieve 100% accuracy, while our passenger and helmet detection models can achieve F1 Scores of 0.99 and 0.97, respectively. Our system can achieve 0,93 in video testing accuracy.
Summarizing Documents on SNS using Successive KeyGraph

Social networking services (SNS) are important sources of news for the world. By summarizing text on SNS, we will be able to grasp what topics in the news are going on. However, because texts keep growing in SNS, we need to process huge amounts of data very efficiently. Therefore, it is necessary to apply a fast and low-memory algorithm. In this investigation, we propose Successive KeyGraph as a method of keywords extraction. The extracted keywords work effectively for news summarization. In Successive KeyGraph, we update keygraph over time. KeyGraph extracts the author’s claims and outputs the results immediately. Also, we hold co-occurrence count which we need to make a keygraph current by using Lossy Counting. We apply several preprocessing to manage unknown words and to improve morphological analysis.
Power-Efficient Beam Tracking During Connected Mode DRX in mmWave and Sub-THz Systems

Discontinuous reception (DRX), wherein a user equipment (UE) temporarily disables its receiver, is a critical power saving feature in modern cellular systems. DRX is likely to be aggressively used at mmWave and sub-THz frequencies due to the high front-end power consumption. A key challenge for DRX at these frequencies is blockage-induced link outages: a UE will likely need to track many directional links to ensure reliable multi-connectivity, thereby increasing the power consumption. In this paper, we explore bandit algorithms for link tracking in connected mode DRX that reduce power consumption by tracking only a fraction of the available links, but without adversely affecting the outage and throughput performance. Through detailed, system level simulations at 28 GHz (5G) and 140 GHz (6G), we observe that even sub-optimal link tracking policies can achieve considerable power savings with relatively little degradation in outage and throughput performance, especially with digital beamforming at the UE. In particular, we show that it is feasible to reduce power consumption by 75% and still achieve up to 95% (80%) of the maximum throughput using digital beamforming at 28 GHz (140 GHz), subject to an outage probability of at most 1%.
Simulation Study on the Effect of Constant Hole Length of Curved Diverging Collimators for Radiation Monitoring Systems

The nuclear accident that occurred in Fukushima, Japan, in 2011, resulted in considerable radiation leaks and mass exposure owing to an insufficient initial response to the disaster. To prevent similar disasters in the future, their cause should be identified, analyzed, and addressed using radiation-monitoring systems. The core component of a radiation-monitoring system is the gamma camera. It consists mainly of a gamma detector and a collimator. We proposed a diverging collimator that incorporates a novel concept of a curved surface such that the collimator hole length is constant for all the incident hole angles. Therefore, the sensitivity is likely to be more uniform across the field of view (FOV) compared with the case of the conventional flat collimator. Through a simulation study, we determined the collimator parameters and compared the performance of the curved collimator with that of the conventional flat collimator. The results revealed the feasibility of achieving higher uniform sensitivity, up to 10.5% at the edge of the FOV by using the proposed curved collimator. The average peak-to-background ratio (PBR) of the curved collimator was determined to be higher by 10.8% across the entire FOV. This indicates that it produced a more distinct signal than the flat collimator. In our future research, we intend to fabricate a curved collimator and evaluate its performance experimentally.
Generating Healthcare Time Series Data for Improving Diagnostic Accuracy of Deep Neural Networks

Data scarcity and class imbalance are common occurrences in healthcare datasets and have an adverse effect on classification performance of machine learning models. Artificial data generation in various applications can be used to handle these challenges. This article proposes a guided evolutionary synthesizer (GES), a tool derived from principles of genetic algorithm, and designed to generate artificial healthcare time series data for improving classification performance of machine learning models. We conducted a series of promising and confirmatory preliminary experiments performance using traditional machine learning and nonresidual convolutional neural network models to evaluate the effectiveness of GES synthetic on data classification. Motivated by the preliminary results, we conducted eight detailed experiments using residual neural network (ResNet), which demonstrated the flexibility of GES and the effectiveness of GES synthetic data in improving the classification performance of deep neural networks. These experiments use GES generated electrocardiogram (ECG) and electroencephalogram (EEG) datasets. Our findings show that models trained with GES synthetic data performed better than models trained with regular perturbed data, had better diagnostic performance for both EEG and ECG datasets, achieved better performance with lower training volume for EEG dataset, eased learning biases seen in the literature for ECG normal sinus and premature ventricular complex rhythms, and had better classification outcomes than comparable related work models trained with similar ECG classes.
Social event activity recommendation mechanism for location-based services

The phenomenon of "impulse purchasing" or "impulse shopping" is an unplanned decision to buy or to shop based on the context (e.g. emotion, feelings, event etc.) at that time. For example, when people have just finished watching a ball game, they are more likely to purchase game-related products in that time than they would in a normal situation. In this paper, we proposed a novel social activity recommendation mechanism for enhancing location- based e-commerce, by incorporating the techniques of social event analysis, context characteristic analysis and activity fitness analysis. The proposed mechanism could identify the most social event for targeted users in a changing environment, and provide the users "the most willing to attend" event activity in a more intellective way.
OP-NCON170015 1..11

For centuries, people have asked questions to hand-held pendulums and interpreted their movements as responses from the divine. These movements occur due to the ideomotor effect, wherein priming or thinking of a motion causes muscle movements that end up swinging the pendulum. By associating particular swinging movements with “yes” and “no” responses, we investigated whether pendulums can aid decision-making and which personality traits correlate with this performance. Participants (N 1⁄4 80) completed a visual detection task in which they searched for a target letter among rapidly presented characters. In the verbal condition, participants stated whether they saw the target in each trial. In the pendulum condition, participants instead mentally “asked” a hand-held pendulum whether the target was present; particular motions signified “yes” and “no”. We measured the accuracy of their responses as well as their sensitivity and bias using signal detection theory. We also assessed four personality measures: locus of control (feelings of control over one’s life), transliminality (sensitivity to subtle stimuli), need for cognition (preference for analytical thinking), and faith in intuition (preference for intuitive thinking). Overall, locus of control predicted verbal performance and transliminality predicted pendulum performance. Accuracy was low in both conditions (verbal: 57%, pendulum: 53%), but bias was higher in the verbal condition (d 1⁄4 1:10). We confirmed this bias difference in a second study (d 1⁄4 0:47, N 1⁄4 40). Our results suggest that people have different decision strategies when using a pendulum compared to conscious guessing. These findings may help explain why some people can answer questions more accurately with pendulums and Ouija boards. More broadly, identifying the differences between ideomotor and verbal responses could lead to practical ways to improve decision-making.
Three-Stage Breathing Effort Quantification for Obstructive Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals

A level-IV home-based sleep apnea monitoring system that utilizes alternative sensors, such as dual respiratory inductance plethysmography (RIP) belts, is proposed to promote routine apnea monitoring. Notably, continued excursion may occur in RIP belt signals, owing to the imperfect relationship between thoracic and abdominal movements during obstructive events. Therefore, we propose a novel algorithm to detect obstructive apnea based on an obstructive reciprocal divergence (ORD) continued excursion model and to explore the possibility of a multistage breathing-effort evaluation model using only RIP signals. Using the developed approach, we detected obstructive sleep apnea with a high accuracy of 99.83 ± 0.71% and a slight reduction of 73.34 ± 28.35% in hypopnea performance with overall combined objective metrics of 89.38 ± 10.53%. We found that introducing many stages improves specificity (p < 0.001). Furthermore, apart from apneic detection, we detected subtle changes in RIP signals qualitatively, which can help represent the inspiratory flow limitation (IFL) of the RIP. This study was validated by predicting an apnea hypopnea index (AHI) based on paradoxical breathing during sleep. A strong exponential relationship was observed between the proposed parameter based on the number of transitions with AHI ( $\text{R}^{2}=0.98$ ; p < 0.001). The proposed approach can assist sleep technologists in characterizing obstructive and nonobstructive apneic events. Moreover, ORD is competent for further quantitative and qualitative IFL analysis and will significantly benefit the automated IFL detection system studies.
Understanding the Influence of Power Transformer Faults on the Frequency Response Signature Using Simulation Analysis and Statistical Indicators

Frequency Response Analysis (FRA) is the most reliable technique currently used to evaluate the mechanical integrity of power transformers. While the measurement devices have been well developed over the past two decades, interpretation of the FRA signatures is still challenging regardless of the several papers published in this regard. This paper adds an attempt to understand the power transformer FRA signatures through experimental and simulation analyses. In this context, experimental FRA measurements are conducted on a 33/11 kV, 30 MVA transformer under various faults, including winding deformation, the short circuit turns, loss of clamping, and bushing fault. At the same time, the high-frequency transformer model that comprises series capacitance, self-inductance, series resistance, and mutual inductance is simulated using MATLAB / Simulink to compare simulation and experimental results. The correlation between physical circuit parameters and various faults facilitates a better understanding of each fault’s effect on the FRA signature. To quantify the impact of such faults, correlation coefficient, the absolute sum of logarithmic error, standard deviation, and sum square error are calculated with respect to the healthy signature at three frequency regions. Results show that using statistical coefficients over three frequency ranges of the FRA signature facilitates better fault identification and quantification.
Monitoring And Controlling Smart Hidroponics Using Android and Web Application

Hydroponics is the cultivation of planting using water without using soil by emphasizing meeting the nutritional needs of plants. Nutrition and water play an important role for hydroponic plants. This hydroponic system has the advantage that it can be used to overcome the problem of an increasingly narrow land shortage. The most important thing in a hydroponic planting system is the process of monitoring the amount of plant nutrients and the (Power of The Hydrogen) pH content of the water which must match the needs of the plant, otherwise the plant growth is less than optimal. Therefore we need a system that can monitor and control the needs of the amount of plant nutrition, pH of the water, and the temperature of the plant's environment. Through smart hydroponics which uses a (Total dissolved solid) Tds meter to measure the content of nutrients dissolved in water, a DIY more sensor to measure the pH of water, as well as the Ds18b20 sensor which is used to measure the environmental temperature sensor and is connected to the Arduino Uno as a microcontroller so the system can monitor hydroponic plant parameters. in real time. In addition, by utilizing Firebase as a database, smart hydroponics can be monitored via Android and web applications. Based on the results of the hydroponic smart test that has been built, it can help hydroponic farmers in monitoring the growth of their plants so that they can produce maximum yields.
A Neural Network in Convolution with Constant Error Carousel Based Long Short Term Memory for Better Face Recognition

Article History: Received: 10 November 2020; Revised: 12 January 2021; Accepted: 27 January 2021; Published online: 05 April 2021 Abstract: Unconstrained face identification, facial periocular recognition, facial land marking and pose prediction, facial expression recognition, 3D facial model design, and other facial-related problems require robust face detection in the wild. Despite the fact that the face recognition issue has been researched intensively for decades with different commercial implementations, it nevertheless faces problems in certain real-world scenarios due to multiple obstacles, such as severe facial occlusions, incredibly low resolutions, intense lighting, exceptionally pose inconsistencies, picture or video compression artefacts, and so on. To solve the problems described above, a face detection technique called Convolution Neural Network with Constant Error Carousel dependent Long Short Term Memory (CNN-CEC-LSTM) is proposed in this paper. This research implemented a novel network structure and designed a special feature extraction that employs a selfchannel attention (SCA) block and a self-spatial attention (SSA) block that adaptively aggregates the feature maps in both channel and spatial domains to learn the inter-channel and inter-spatial connection matrices; additionally, matrix multiplications are conducted for a This approach first smoothed the initial image with a Gaussian filter before measuring the gradient image. The Canny-Kirsch Method edge detection algorithm was then used to identify human face edges. The proposed method is evaluated against two recent difficult face detection databases, including the IIT Kanpur Dataset. The experimental findings indicate that the proposed approach outperforms the most current cutting-edge face recognition approaches.
BIOLOGICALLY SYNTHESISED COPPER OXIDE AND ZINC OXIDE

13 The management of Lyctus africanus, one of the major dry wood pests in the tropical 14 region is difficult due to its secluded habits and long lifecycle and therefore, its control measures 15 are limited to the usage of insecticides. The insecticides particularly the metal salts are effective, 16 but in some cases their leaching leads to concerns about environmental pollution. Nanometal 17 particles are found to be more effective than metal salts. Presently available metal nanoparticles 18 are synthesized using physical or chemical methods and their production results in toxic by19 products and are costly. The current investigation deals with synthesis and use of metal 20 nanoparticle for wood protection in an environmentally friendly and cost-effective way. The 21 plant extracts that are reported to have wood preservative properties were used for the synthesis 22 of metal nanoparticles. Copper oxide and zinc oxide nanoparticles were synthesized using leaf 23 extracts of Lantana camara. The efficacy of the synthesized Lantana camara leaf extract and 24 copper oxide or zinc oxide nanoparticle formulation as a wood protectant was tested against 25 Lyctus africanus as per BIS 4873 Part 2. The formulation of copper oxide nanoparticle and 26 Lantana camara leaf extract effectively protected the treated rubberwood blocks from Lyctus 27 africanus attack, when compared to zinc oxide nanoparticle Lantana camara leaf extract 28 formulation and can be developed into a stable, ecofriendly wood preservative. 29