Miracle-dz/UniFAR-FTUs huggingface dataset:
Desain Power System Stabilizer Berbasis Fuzzy dan Particle Swarm Optimization

One important factor to produce a qualified electricity is the stability of the system. An unstable system resulted an undamped oscilation of system, and the stable system can damp the oscilation quickly. Therefore, it is necessary to apply a stability device to a power system and it is called a Power System Stabilizer (PSS). One of stability design is a feedback control design. Here, in this research, the state feedback control are designed for Single Machine Infinite Bus (SMIB) . The SMIB model is non linear therefore the feedback control can’t be designed directly. Some researchers do linearize the system before design the feedback control. In this research, a nonlinear model of SMIB is build in a state space form. Subsequently, a fuzzification Takagi-Sugeno is applied. The state feedback controls are applied to design the control of SMIB fuzzy system, a state feedback gain is determined using method Routh Hurwitz. The determining the parameter of state feedback gain influence the performance of SMIB. Therefore, it is important to determine the suitable parameter such that the SMIB has the optimal performance. The Particle Swarm Optimization (PSO) is applied to optimaze the performance of SMIB. In these research, it is compared the performance of SMIB by applying between Routh Hurwitz, fuzzy Routh Hurwitz, PSO fuzzy Routh Hurwitz for state feedback control. The simulation result show that Performance of SMIB using The PSO Fuzzy Routh Hurwitz state feedback can improve the performance of SMIB, but the performance of Efd become oscillate and this method influence by the chosen parameter.
Network modeling of single-cell omics data: challenges, opportunities, and progresses

Abstract Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell–cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.
A Non-learning-oriented Recognition Algorithm of Vehicle License Plate Characters

Based on the fact that characters of vehicle license plate are standardized, a recognition algorithm of Vehicle License Plate Characters based on non-learning-oriented method is proposed. This method uses some concepts such as Shape Feature Vector (SFV, for short) to describe the shape of the to be recognized characters. Each character will be evaluated with a vector and the recognition is realized by calculating the similarity of vectors. This method achieves character recognition by vector arithmetic avoiding the learning/training process often used in some algorithms, such as artificial neural network, and SVM and so on. The recognition is simplified tremendously. The feasibility of using SFV to recognize vehicle license plate characters is proven theoretically, and then, the character recognition algorithm of vehicle license plate is evaluated by simulation. The result of the simulation shows that SFV can be used as license plate character recognition. And the license plate recognition algorithm based on SFV has an accuracy rate of 97.31%. This method is a helpful attempt of character recognition based on non-learning-oriented method.
Validation of a lookup-table approach to modeling turbine fatigue loads in wind farms under active wake control

Abstract. Wake redirection is an active wake control (AWC) concept that is known to have a high potential for increasing the overall power production of wind farms. Being based on operating the turbines with intentional yaw misalignment to steer wakes away from downstream turbines, this control strategy requires careful attention to the load implications. However, the computational effort required to perform an exhaustive analysis of the site-specific loads on each turbine in a wind farm is unacceptably high due to the huge number of aeroelastic simulations required to cover all possible inflow and yaw conditions. To reduce this complexity, a practical load modeling approach is based on “gridding”, i.e., performing simulations only for a subset of the range of environmental and operational conditions that can occur. Based on these simulations, a multi-dimensional lookup table (LUT) can be constructed containing the fatigue and extreme loads on all components of interest. Using interpolation, the loads on each turbine in the farm can the be predicted for the whole range of expected conditions. Recent studies using this approach indicate that wake redirection can increase the overall power production of the wind farm and at the same time decrease the lifetime fatigue loads on the main components of the individual turbines. As the present level of risk perception related to operation with large yaw misalignment is still substantial, it is essential to increase the confidence level in this LUT-based load modeling approach to further derisk the wake redirection strategy. To this end, this paper presents the results of a series of studies focused on the validation of different aspects of the LUT load modeling approach. These studies are based on detailed aeroelastic simulations, two wind tunnel tests, and a full-scale field test. The results indicate that the LUT approach is a computationally efficient methodology for assessing the farm loads under AWC, which achieves generally good prediction of the load trends.
Population Statistics Algorithm Based on MobileNet

In today’s society, intelligent video surveillance plays an important role in social security, traffic scheduling, national security and other fields. One of the research hotspots is people statistics based on image processing, which has strategic significance in practical applications. Aimed at the problem that the low accuracy in the actual application scenario, the limited hardware resources, and the low operation efficiency, this paper proposes a multi-feature target detection model based on the lightweight deep learning network MobileNet [1], which can be used in intelligent terminals. The basic feature-extraction network MobileNet as a lightweight network can provide a flexible alternative configuration in terms of efficiency and accuracy. The underlying detection network selects a single deep nerual network, named SSD [2]. The algorithm can achieve multi-scale target detection, and uses the target position and category to perform one-time regression. In this paper, the activation function of SSD is changed into SeLU (scaled exponential linear units) [3], which improves the robustness of the algorithm. At the same time, the work of sample diversity and data enhancement has been made, and the characteristics of the human body above the shoulders have been fully utilized. Experiments have shown that the improved network structure based on MobileNet has higher detection accuracy, lower delay, excellent robustness, while the number of model parameters is effectively reduced.
RANSAC lens boundary feature based kernel SVM for transparent contact lens detection

Transparent contact lens spoofing has been demonstrated to hamper the overall performance of an iris recognition system. Achieving high detection accuracy with transparent lens is quite challenging using iris texture analysis based techniques. In this regard, the authors propose a supervised learning based transparent lens detection method by designing a novel set of features to describe the faint lens boundary. The input image is first segmented for extracting salient edge points in the sclera region of interest. RANSAC algorithm then performs circle fitting to generate the set of R-points for feature computation. The novel features such as concentricity parameter, R-point count, R-point intensity, R-circle radius, R-cluster count and circle genuineness are associated with distinctive range of values for each class. Experimental results obtained using the kernel-support vector machine (SVM) classifier show that the proposed method can achieve higher average detection accuracy as compared to state-of-the-art techniques: 90.63% (NotreDame I), 84.5% (NotreDame II), 83% (IIIT-D Cogent) and 86.27% (IIIT-D Vista).
FaceInput: A Hand-Free and Secure Text Entry System through Facial Vibration

Wearable wristbands have become prevailing in the recent days because of their small and portable property. However, the limited size of the touch screen causes the problems of fat fingers and screen occlusion. Furthermore, it is not available for users whose hands are fully occupied with other tasks. To break this bottleneck, we propose a portable, hand-free and secure text-entry system, called FaceInput, which firstly uses a single small form factor sensor to accomplish a practical user input via facial vibrations. To sense the tiny facial vibration signals, we design and implement a double-stage amplifier whose maximum gain is 225. To enhance the input accuracy and robustness, we design a set of novel schemes for FaceInput based on the Mel-frequency cepstral coefficient (MFCC) concept and a hidden Markov model (HMM) to process the vibration signals, and an online calibration and adaptation scheme to recover the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects during the period of one month. The results demonstrate that FaceInput can be successful to sense the tiny facial vibrations and robust to fight against various confounding factors. The average recognition accuracy is 98.2%. Furthermore, by enabling the runtime calibration and adaptation scheme that updates and enlarges the training data set, the accuracy can reach 100%.
Implementation Hybrid (NIDS) System using Anomaly Holtwinter Algorithm and Signature based Scheme

_______________________________________________________________________________ ABSTRACT An intrusion detection system is a security attack detection/prevention mechanism, it can be implemented into the software module or hardware module for the purpose of monitoring the systems or network for malicious activities. IDS can be categorized by monitoring resources to Host Intrusion Detection System (HIDS) and Network Intrusion Detection System (NIDS). HIDS are deployed to monitor local activities on the speciﬁc machine; on the other hand, NIDS placed into the central point on the network such as ﬁrewall to monitor network tra ﬃ c. IDS also can be categorized depending on the detection method for anomaly and misuse (signature). In this paper, we implement a hybrid implementation of IDS using Holt-winter anomaly algorithm and signature-based approach. Furthermore, a case study using Holt-winter anomaly based and signature misuse based schemes will be implemented and analyzed, finally, the result of the experiment will be shown.
An Improved 4H-SiC Trench MOS Barrier Schottky Diode With Lower On-Resistance

An improved 4H-SiC trench MOS barrier Schottky (TMBS) structure that can significantly reduce the specific on-resistance (<inline-formula> <tex-math notation="LaTeX">$R_{on,sp}$ </tex-math></inline-formula>) is proposed in this paper. Compared with the conventional TMBS structure, an N-type region is added around the P+ shielding region and along the trench sidewall in the proposed structure, and the doping concentration is higher than that of the drift region. The JFET resistance, and spread resistance can both be reduced significantly during the forward bias to produce a much lower <inline-formula> <tex-math notation="LaTeX">$R_{on,sp}$ </tex-math></inline-formula>, which have been verified through the numeric simulations and modeling analysis. The critical parameters (N-type region width and its doping concentration) of the proposed structure are mainly optimized by the ATLAS simulation tool. As a result, the breakdown voltage and leakage current of the improved structure remain basically unchanged compared with the conventional TMBS. However, the <inline-formula> <tex-math notation="LaTeX">$R_{on,sp}$ </tex-math></inline-formula> and FoM (<italic>BV</italic><sup>2</sup>/<inline-formula> <tex-math notation="LaTeX">$R_{on,sp}$ </tex-math></inline-formula>) can be effectively improved by 41.9% and 70.9%, respectively.
Deriving Tourist Mobility Patterns from Check-in Data

Recommendations in complex scenarios require additional knowledge of the domain. Planning a composite travel spanning several countries is a challenging, but encouraging domain for recommender systems, since users are in dire need for assistance: Information in typical publications, such as printed travel guides or personal blogs is often imprecise, biased or outdated. In this paper we motivate a data-mining approach to improve destination recommender systems with learned travel patterns. Specifically, we propose a methodology to mine trips from location-based social networks to improve recommendations for the duration of stay at a destination. For this we propose a model for combining data from different sources and identify several metrics that are useful to ensure sufficient data quality, i.e., whether a traveler's check-in behavior is adequate to derive patterns from it. We demonstrate the utility of our approach using a Foursquare data set from which we extract 23,418 trips in 77 countries. Analyzing these trips, we determine the travel durations per country, how many countries are typically visited in a given time span and which countries are often visited together in a composite trip. Also, we discuss how this method can be generalized to other recommender systems domains.
Operation quality assessment model for video conference system

Video conference system has become an important support platform for smart grid operation and management, its operation quality is gradually concerning grid enterprise. First, the evaluation indicator system covering network, business and operation maintenance aspects was established on basis of video conference system’s operation statistics. Then, the operation quality assessment model combining genetic algorithm with regularized BP neural network was proposed, which outputs operation quality level of the system within a time period and provides company manager with some optimization advice. The simulation results show that the proposed evaluation model offers the advantages of fast convergence and high prediction accuracy in contrast with regularized BP neural network, and its generalization ability is superior to LM-BP neural network and Bayesian BP neural network.
Implementation of Blockwork System in Malaysia

Industrialised Building System or IBS is a method to accelerate the process of construction through prefabricated concept where each component will be built first in the component manufacturing plant. There are six categories of IBS introduced by CIDB and one of them is a system block work. The block system has several types of components under this category include the interlocking block-concrete masonry unit (CMU) and lightweight concrete blocks. Block work system introduced to replace the use of conventional bricks to accelerate the construction process and reduce construction costs. However, acceptance of the system to be less favorable than the use of conventional bricks. Statistics show there are only 31 manufacturer across Malaysia. Therefore, this study aims to identify the extent of implementation of the system of block work in Malaysia to contribute in the construction sector. This study used qualitative methods, namely through interviews and focus groups. The findings revealed that there are differences between the application methods IBS system block and conventional brick in terms of type of work performed, the number of working days and the cost of construction. Implementation of the system blocks are also influenced by three factors: the skills of workers, quality of components and component types in use. With a wide exposure is expected to expand the implementation of the system blocks in Malaysia.
Object Detection under Natural Illumination Conditions using Superpixels and Local Binary Pattern Feature

This paper proposes a novel object detection method under natural illumination conditions in which a set of features, texture features and Local Binary Pattern (LBP) features, are extracted from the images acquired by a colour camera. The goal of this study is to develop a robust and fast algorithm to detect immature green citrus fruit in individual trees from colour images acquired under natural outdoor conditions. Colour and shape features are used to remove the complex background as much as possible. Since leaves share much more similarities with green citrus fruit in colour and to some extent in the shape, texture features are used for citrus detection. Statistical features, Tamura features and LBP features are used to build the KNN classifier. Experimental results show that the proposed approach provides fairly good object detection performance and confirms an efficient way for outdoor green citrus detection.
Cost-efficient dynamic quota-controlled routing in multi-community delay-tolerant networks

Delay-tolerant networks are novel wireless mobile networks, which are characterized with high latency and frequent disconnectivity. Besides, people carrying mobile devices form a lot of communities because of similar interests and social relationships. How to improve the routing efficiency in multi-community scenarios has become one of the research hot spots in delay-tolerant networks. In this article, we present a network model of the multi-community delay-tolerant networks and formulate a dynamic quota-controlled routing problem of minimizing the average number of copies of a message that satisfies the required delivery probability under the given time-to-live of the message as a nonlinear optimization problem. To solve this problem, we propose an improved genetic algorithm called genetic algorithm for delivery probability and time-to-live optimization for the dynamic quota-controlled routing scheme to reduce the routing cost further. In addition, a cost-efficient dynamic quota-controlled routing protocol based on genetic algorithm for delivery probability and time-to-live optimization is proposed, which can dynamically adjust message copies according to its assigned delivery probability and time-to-live in different communities on the shortest path. Both the numerical and simulation results show that our routing with the proposed algorithm is more cost efficient.
A Novel Hierarchical Fuzzy Systems Based Cooperative Spectrum Sensing and Opportunistic Spectrum Access Techniques

When a large number of cognitive users (CUs) are competing for the spectral resource sharing with different parameters, it is a challenging job to decide a scheduling criterion to be followed and to solve the problem of idle channel allocation. This paper proposed a novel two levels Hierarchical Fuzzy Systems based cooperative spectrum sensing (level 1) and opportunistic spectrum access (level 2). Three separate groups of hierarchical fuzzy systems has been designed to estimate the probability of presence of three primary users (PUs) in first controller level and then estimate the percentage ratio of allocating idle channels to three out of nine CUs in second controller level. To improve the performance of this system during mobility state of PUs and/or CUs, a Hopping System module is proposed. The CUs nodes dynamically coordinate their action to detect all the bands sequentially in organized manner. The results indicate that fuzzy logic can be used in cooperative spectrum sensing to provide additional flexibility in decision making. While in opportunistic spectrum access, fuzzy logic allow the CU with highest possibility of spectrum access to occupy the idle channel and improve the probability of detection in mobility state by using hopping code.
Optimizing Haptic Human-Robot Collaboration Considering Human Perception and Idiosyncrasies

The interaction of humans and robots aims to overcome emerging demands of flexible production, demographic change, and improvement of life in general. A promising form of complementary collaboration is haptic Human-Robot Collaboration. This thesis modeled individual manipulation behavior of different sized objects as well as mass perception and provides methods for objective and subjective evaluation. The perception-related assistance strategy framework is introduced to provide engineers and researchers with a basis for designing human-robot collaboration that enables high usability and acceptance of the robotic system.
Signal- and expectation-based processing of pitch accent types. Poster presentation.

In many languages speakers employ prosody to highlight new or surprising information, making it more prominent, while they attenuate given or expected information, making it less prominent. For German it has been shown that pitch accent types differ in their degree of perceived prominence [1] and play an important role in the marking of information status (e.g. [2]). The pitch movement leading towards the target on the accented syllable (‘onglide’ [3
Assessment of Hand Gestures Using Wearable Sensors and Fuzzy Logic

Hand dexterity and motor control are critical in our everyday lives because a significant portion of the daily motions we perform are with our hands and require some degree of repetition and skill. Therefore, development of technologies for hand and extremity rehabilitation is a significant area of research that will directly help patients recovering from hand debilities sustained from causes ranging from stroke and Parkinson’s disease to trauma and common injuries. Cyclic activity recognition and assessment is appropriate for hand and extremity rehabilitation because a majority of our essential motions are cyclic in their nature. For a patient on the road to regaining functional independence with daily skills, the improvement in cyclic motions constitutes an important and quantifiable rehabilitation goal. However, challenges exist with hand rehabilitation sensor technologies preventing acquisition of long-term, continuous, accurate and actionable motion data. These challenges include complicated and uncomfortable system assemblies, and a lack of integration with consumer electronics for easy readout. In our research, we have developed a glove based system where the inertial measurement unit (IMU) sensors are used synergistically with the flexible sensors to minimize the number of IMU sensors. The classification capability of our system is improved by utilizing a fuzzy logic data analysis algorithm. We tested a total of 25 different subjects using a glove-based apparatus to gather data on two-dimensional motions with one accelerometer and three-dimensional motions with one accelerometer and two flexible sensors. Our research provides an approach that has the potential to utilize both activity recognition and activity assessment using simple sensor systems to help patients recover and improve their overall quality of life.