Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of HVDC Technology with Technical Challenges for Economic Dispatch

High Voltage Direct Current (HVDC) technology has proven to be a techno-economic means of meeting high power demand while ensuring the reliability and sustainability of modern power systems. This paper addresses Economic Dispatch for HVDC incorporating the technical challenges of voltage instability, difficulty in protecting Direct Current (DC) systems, and the losses involved. A cost function is formulated to include fuel cost due to optimal power generation of each generating unit, a revenue loss term and a compensation cost due to a power outage caused by the addressed challenges. A comparison is also made between Economic Dispatch (ED) for HVDC and High Voltage Alternating Current (HVAC) considering losses. Novel Bat Algorithm (NBA) has been used to solve the problem and its effectiveness tested on IEEE 6-generator 30-bus system.
Integrated Nonlinear System Framework for Air Vehicle Autonomy Design and Validation

Physical systems such as air vehicles are inherently nonlinear and complex. A complete treatment of air vehicle from design, modeling, analysis, and simulation until control design for vehicle autonomy demands, therefore, application of tools from nonlinear dynamics and control theories. Blending dynamical systems theory and nonlinear control techniques provides a unique and physically intuitive methodology for developing control algorithms for various complex tasks. Our recent works on highly nonlinear aircraft models have led to the development of a generic simulation platform for guidance, control, and navigation of six degree-of-freedom (dof) air vehicles in a completely nonlinear framework. An overview of the nonlinear framework involved in the development of the unique platform and its applications to maneuvering and waypoint navigation of two vehicle models are presented in the paper.
Pharmacophore modeling, comprehensive 3D-QSAR, and binding mode analysis of TGR5 agonists

Abstract Takeda G-protein-coupled receptor 5 (TGR5) is emerging as an important and promising target for the development of anti-diabetic drugs. Pharmacophore modeling and atom-based 3D-QSAR studies were carried out on a new series of 5-phenoxy-1,3-dimethyl-1H-pyrazole-4-carboxamides as highly potent agonists of TGR5. The generated best six featured pharmacophore model AAHHRR consists of two hydrogen bond acceptors (A): two hydrophobic groups (H) and two aromatic rings (R). The constructed 3D-QSAR model acquired excellent correlation coefficient value (R2 = 0.9018), exhibited good predictive power (Q2 = 0.8494) and high Fisher ratio (F = 61.2). The pharmacophore model was validated through Guner–Henry (GH) scoring method. The GH value of 0.5743 indicated that the AAHHRR model was statistically valuable and reliable in the identification of TGR5 agonists. Furthermore, the combined approach of molecular docking and binding free energy calculations were carried out for the 5-phenoxy-1,3-dimethyl-1H-pyrazole-4-carboxamides to explore the binding mode and interaction pattern. The generated contour maps revealed the important structural insights for the activity of the compounds. The results obtained from this study could be helpful in the development of novel and more potent agonists of TGR5.
Development of inert ceramic for industrial application based on ternary phase diagram of potassiun oxide-aluminum oxide-silicon dioxide

An example of sintered inert ceramic is ceramic balls as catalyst support media. The composition of the ceramic is regulated by Universal Oil Products (UOP) standard. The requirement of the chemical composition will therefore determine the range of sintering temperature, number of phases or crystal during sintering, and the weight percent of starting materials. The weight percent of starting materials becomes important in order to fulfil the UOP requirement since all of the materials are stable raw materials and can be found directly from nature. This study is to design how to have the starting material composition and sintering temperature to fulfil the standards. One example of inert ceramic ball design is illustrated at the end of this paper.
Measured and Extraction of Coupling Capacitive of Metal Interconnect Layers in VLSI

This paper presents the measurement and extraction of the capacitance for multilayer interconnections in VLSI. The capacitance of the capacitive coupling between interconnect is measured as a function of the varying frequency in order to develop a capacitance versus frequency curve. The multi-fringe structure of interconnect metal1 layer and interconnect metal2 layer $\pmb{(\text{L}=400\mu \text{m}, \ \text{no}. \ \text{strip} =80)}$ with the total perimeter of capacitive coupling is approximately $\pmb{3.16\times 10^{4}} \pmb{\mu} \mathbf{m}$ of value and the area is $\pmb{2.8\times 10^{4}} \ \pmb{\mu\text{m}^{2}}$ of value are tested respectively. The line width and line spacing ratio (W/S) of interconnect is varying from 0.9/0.6, 0.9/0.9, 0.9/1.05 and 0.9/1.2 with the measured frequency of 10kHz, 100kHz, 250kHz, 500kHz, IMHZ and 5 MHz are measured. The coupling of interconnect per unit length at various line spacing as a function of measured frequency of metal1 and metal2 are reported and discussed. The results show that the coupling capacitance per unit length $\pmb{(\text{F}/\mu \text{m})}$ increases as the spacing between adjacent interconnect lines is deceased.
SoCINT: Resilient System-on-Chip via Dynamic Intrusion Detection

Modern multicore System-on-Chips (SoCs) are regularly designed with third-party Intellectual Properties (IPs) and software tools to manage the complexity and development cost. This approach naturally introduces major security concerns, especially for those SoCs used in critical applications and cyberinfrastructure. Despite approaches like split manufacturing, security testing and hardware metering, this remains an open and challenging problem. In this work, we propose a dynamic intrusion detection approach to address the security challenge. The proposed runtime system (SoCINT) systematically gathers information about untrusted IPs and strictly enforces the access policies. SoCINT surpasses the-state-of-the-art monitoring systems by supporting hardware tracing, for more robust analysis, together with providing smart counterintelligence strategies. SoCINT is implemented in an open source processor running on a commercial FPGA platform. The evaluation results validate our claims by demonstrating resilience against attacks exploiting erroneous or malicious IPs.
SpringLoc: A Device-Free Localization Technique for Indoor Positioning and Tracking Using Adaptive RSSI Spring Relaxation

Device-free localization (DFL) algorithms using the received signal strength indicator (RSSI) metrics have become a popular research focus in recent years as they allow for location-based service using commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities, or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc and a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low-node deployment density.
3D Region Merging for Segmentation of Teeth on Cone-Beam Computed Tomography Images

Segmentation of teeth in Cone-Beam Computed Tomography (CBCT) images is challenging problem due to its noise and the similar grayscale intensity of bone and teeth element. In this paper we proposed a new method based on three-dimensional (3D) region merging and histogram thresholding for automatic segmentation of teeth on CBCT images. The proposed 3D region merging algorithm can recognized the teeth element that have similar intensity with the bone element based on the three-dimensional (3D) information of the neighboring slices of the CBCT image. Merging the teeth region will lead to more homogenous grayscale intensity distribution inside the teeth. Then histogram thresholding that utilized the characteristic of CBCT images is performed to binarize the grayscale images and obtain the teeth object. The average accuracy, sensitivity, and specificity of the proposed method are 97.75%, 80.22%, and 98.31%, respectively. The proposed method is fully automatic, therefore lead to more objective and reproducible results.
Physical Synthesis of Flow-Based Microfluidic Biochips Considering Distributed Channel Storage

Flow-based microfluidic biochips (FBMBs) have attracted much attention over the past decade. On such a micrometer-scale platform, various biochemical applications, also called bioas-says, can be processed concurrently and automatically. To improve execution efficiency and reduce fabrication cost, a distributed channel-storage architecture (DCSA) can be implemented on this platform, where fluid samples can be cached temporarily in flow channels close to components. Although DCSA can improve the execution efficiency of FBMBs significantly, it requires a careful arrangement of fluid samples to enable the channels to fulfill the dual functions of transportation and caching. In this paper, we formulate the first flow-layer physical design problem considering DCSA, and propose a top-down synthesis algorithm to generate efficient solutions considering execution efficiency, washing, and resource usage simultaneously. Experimental results demonstrate that the proposed algorithm leads to a shorter execution time, less flow-channel length, and a higher efficiency of on-chip resource utilization for biochemical applications compared with a direct approach to incorporate distributed storage into existing frameworks.
Complex Synchronization of a Ring-Structured Network of FitzHugh-Nagumo Neurons With Single-and Dual-State Gap Junctions Under Ionic Gates and External Electrical Disturbance

Synchronization plays an essential role in processing information and decisions by neurons and their networks in the brain, and it is useful to study the synchronization of neuron networks, as a part of the process of understanding the functionality of both healthy and diseased brains. In the past, most studies had developed control schemes relating to synchronization problems which were limited to two or three neurons, which cannot depict the dynamic synchronization behavior of neuron networks. In this paper, we investigated the synchronization issues associated with a ring-structured network of FitzHugh–Nagumo (FHN) neurons, under external electrical stimulation, and with single- and dual-state gap junctions. In addition, the gap junctions (coupling)s and ionic gate disturbances were included in the dynamics of this FHN neuronal network, making our work both more realistic, and more challenging. Thus, each neuron in this network was influenced synaptically by its neighboring two neurons. A simple, robust, and adaptive control scheme, for both a single- and the dual-gap-junction network has been proposed, which will compensate for the nonlinear dynamics without direct cancelation to achieve synchronization. Sufficient conditions to guarantee synchronization of both membrane potentials and recovery variables were derived by using Lyapunov stability theory. Finally, the proposed scheme was validated, and its efficacy was comprehensively analyzed through numerical simulations.
Micro Expression: Comparison of Speed and Marking Accuracy in Facial Component Detection

In the micro-expression detection system on part of facial components, it is necessary to detect that component in accurately, precisely and fast. This study shows a comparison between accuracy and speed in the detection of the component area of the face (right eye, left eye and mouth) automatically. Micro expression occurs in a short time, fast and in very smooth movements. The face component detection method conducted in this study is a feature-based method (Viola Jones method) which is compared with the model-based method (ASM and DRMF methods). Comparison of the accuracy of the three methods in the detection of the face component area shows that the ASM and DRMF methods provide an accuracy value of 100%, while the Haar Cascade Classifier method shows an average accuracy of 44%. Meanwhile, on speed measurement to find the face component area, the DRMF method is the fastest with an average processing time of 0.08 seconds followed by ASM method of 0.14 seconds, and the Haar method of 0.25 seconds.
VIABILITY SELECTION AT THREE EARLY LIFE STAGES OF THE TROPICAL TREE, PLATYPODIUM ELEGANS (FABACEAE, PAPILIONOIDEAE)

Abstract Given the enormous number and high mortality of fertilized ovules in plants, it is possible that selection during the earliest stages of the life cycle plays an important role in shaping the genetic composition of plant populations. Previous research involving selection component analyses found strong evidence for viability selection in annual plant species. Yet despite this evidence, few attempts have been made to identify the magnitude and timing of viability selection as well as the mechanisms responsible for mortality among genotypes. Platypodium elegans, a Neotropical tree with high rates of early fruit mortality, represents an opportunity to study viability selection at a level of discernment not previously possible. Microsatellite markers were used to analyze the genetic composition of aborted embryos, as well as mature seeds and seedlings of the same cohort. While selection resulted in an overall decrease in self-fertilized progeny across each life stage, the greatest change in the genetic composition of progeny occurred between mature seeds and established seedlings. This suggested that inbreeding depression, and not late-acting self-incompatibility, was responsible for early selection. An investigation of the mature seed stage revealed that self-fertilized seeds weigh significantly less than outcrossed seeds. The result of this early selection conceals the mixed-mating system and high levels of inbreeding depression in Platypodium elegans, resulting in an apparently outcrossed adult population that does not differ significantly from Hardy-Weinberg expectations.
Feature Selection and Negative Binomial Regression for Predicting Number of Defects in Wire Mesh Production

In wire mesh production, many types of defects are found. When the factors related to the number of defects occurring are correctly identified, various improvement methods can then be applied to reduce or control the number of defects. In this paper, the features that are strongly linked with the number of defects are identified by the feature selection process and then used in the prediction process. LASSO method and random forest are applied in the feature selection process. Using selected features from feature selection, a negative binomial generalized linear model (GLM) is employed to predict the number of defects in the mesh manufacturing process. A negative binomial regression is used since the nature of the mesh defect data in this study is count data and over-dispersed. Quality of the selected features from LASSO and random forest are compared using RMSE and RMSLE of the predicted results from the negative binomial regression.
Data Subsetting: A Data-Centric Approach to Approximate Computing

Approximate Computing (AxC), which leverages the intrinsic resilience of applications to approximations in their underlying computations, has emerged as a promising approach to improving computing system efficiency. Most prior efforts in AxC take a compute-centric approach and approximate arithmetic or other compute operations through design techniques at different levels of abstraction. However, emerging workloads such as machine learning, search and data analytics process large amounts of data and are significantly limited by the memory sub-systems of modern computing platforms.In this work, we shift the focus of approximations from computations to data, and propose a data-centric approach to AxC, which can boost the performance of memory-subsystem-limited applications. The key idea is to modulate the application’s data-accesses in a manner that reduces off-chip memory traffic. Specifically, we propose a data-access approximation technique called data subsetting, in which all accesses to a data structure are redirected to a subset of its elements so that the overall footprint of memory accesses is decreased. We realize data subsetting in a manner that is transparent to hardware and requires only minimal changes to application software. Recognizing that most applications of interest represent and process data as multi-dimensional arrays or tensors, we develop a templated data structure called SubsettableTensor that embodies mechanisms to define the accessible subset and to suitably redirect accesses to elements outside the subset. As a further optimization, we observe that data subsetting may cause some computations to become redundant and propose a mechanism for application software to identify and eliminate such computations. We implement SubsettableTensor as a C++ class and evaluate it using parallel software implementations of 7 machine learning applications on a 48-core AMD Opteron server. Our experiments indicate that data subsetting enables 1.33×–4.44× performance improvement with <0.5% loss in application-level quality, underscoring its promise as a new approach to approximate computing.
A Process Engineering Method Based on Ontology and Patterns

Many different process meta-models offer different viewpoints of a same information system engineering process: activity oriented, product oriented, decision oriented, context oriented and strategy oriented. However, the complementarity between their concepts is not explicit and there is no consensus about the concepts themselves. This leads to inadequate process meta-models with organization needs, so the instantiated models do not correspond to the specific demands and constraints of the organizations or projects. Nevertheless, method engineers should be able to build process meta-models according to the specific organization needs. We propose a method to build unified, fitted and multi-viewpoints process meta-models. The method is composed of two phases and is based on a process domain ontology and patterns.
A practical coordinated trajectory tracking control for over actuated systems: a case study for a group of four-wheels omnidirectional mobile robots

Coordination between mobile robots is a mature research topic. Yet, different approaches and new idea in improving stability condition emerge. Other aspect is on the practicability of solution for over actuated mobile robot type. In this work we propose a practical control algorithm that coordinates tracking of the reference trajectories for a group of four-wheels omnidirectional mobile robots. Exploiting the kinematic property of the robot, coordination can be achieved by a combination of inverse based individual trajectory control and information sharing between robots. A globally asymptotically stable error dynamics is proven using Lyapunov approach. A simulation validation shows that the proposed controller can achieved tracking accuracy up to 0.02 [m] and responsive to disturbances occurred in the group.
ISOFLURANE AND PROPOFOL SYNERGY IN ATTENUATING POST-ISCHEMIC MYOCARDIAL INJURY IN PATIENTS UNDERGOING CORONARY ARTERY BYPASS GRAFT SURGERY

Objective: Either isoflurane preconditioning or high dose propofol treatment has been shown to attenuate myocardial ischemiareperfusion injury in patients undergoing coronary artery bypass grafting (CABG) surgery. We hypothesized that joint isoflurane preconditioning and propofol treatment should confer synergistic protective effects in attenuating myocardial injury in patients undergoing CABG surgery using cardiopulmonary bypass (CPB).
Hardware Implementation of AES with S-Box Using Composite-Field for WLAN Systems

The Advanced Encryption Standard (AES) algorithm is the best choice for security services in many applications, such as WLAN systems. This paper presents an AES hardware using composite-field by decomposing the complex Galois-field GF(28) to the lower order field. Conventional designs are based on the Lookup table Sbox (LUT-Sbox)[1] to implement the S-box and InvS-box that cost much memory and area. Our proposed design using combinational logic is based on composite-field in finite fields GF(28). The area cost is reduced because most of the functionalities are shared for both S-box and InvS-box, they are implemented on the same hardware. The proposed implementation occupies 4.1 kGEs and the maximum frequency is 180 MHz. Our implementation uses Verilog HDL on Virtex7 FPGA board.
Low Insertion Loss D-band SPDT Switches Using Reverse and Forward Saturated SiGe HBTs

This paper presents two D-band SPDTs utilizing forward and reverse saturated SiGe HBTs. The SPDT switch designs are based on quarter-wave double shunt topology and state-of-the-art performance are achieved at D-band frequencies from 110 to 170GHz. Measurement results of the reverse saturated SPDT switches show minimum insertion loss of 2dB (including pads losses) at 140GHz and insertion loss better than 2.3dB from 120GHz to 170GHz. The power consumption of both SPDTs is 3.04mW at 0.9V supply.
An Implicit Knowledge Oriented Algorithm for Learning Path Recommendation

Focusing on the lack of implicit knowledge teaching in online teaching activities and combining related research achievements of learning theory and knowledge model, this paper proposes an algorithm for learning path recommendation which is based on ant colony algorithm. Comprehensively considering the students' cognitive style, knowledge basis and group preference, this algorithm takes implicit knowledge as the essential learning goal and recommends personalized learning paths to them. The experimental results prove that the algorithm can effectively improve students' academic performance and learning efficiency.
A Wearable Robotic Object Manipulation Aid for the Visually Impaired

Due to loss of vision, it becomes a challenging task for a visually impaired individual to locate and grasp a target object. This paper presents a hand-worn assistive device that may assist a visually impaired person in detecting a target object and maintaining alignment with the object while approaching it. The device consists of a sensing module and a guiding module. The sensing module uses an RGB-D camera to detect and track the target object. The guiding module computes hand-object misalignment, determines the desired hand movement (DHM) for hand-object alignment, and uses a cable-driven exoskeleton mechanism to guide the user's hand to align with the target object. The guiding module helps to maintain hand-object alignment while the hand is approaching the object. A prototype of the device was developed and its usability was validated by experiments with human subjects.