Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Separation Principle Based $H_\infty$  Observer–Controller Design for a Class of T–S Fuzzy Systems

The observer-based controller design for a T–S fuzzy system subject to external disturbance is investigated in this paper. First, an <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> observer is designed to estimate the unknown system states, by a transformation process for system states and the disturbance term, a state feedback <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> controller is given. Then, the separation principle for a T–S fuzzy system subject to external disturbance is proposed. With the help of the improved separation principle, the observer and controller can be combined together to make the fuzzy system input-state stable, while the <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> index can also be guaranteed. The proposed design approach is applicable for both known and unknown premise variables. In stability analysis, the membership functions derivatives dependent method is proposed to reduce the conservatism. In the simulation part, stability analysis of a three-rule system is carried out to illustrate the conservatism reduction effects in Example <xref ref-type="example" rid="example1">1</xref>; then, a vessel dynamic positioning control process subject to ocean disturbance is adopted to illustrate the effectiveness of the proposed methods for both known/unknown premise variable cases.
Temperature-light-dependent JV and TPV analysis of pure sulfide based Cu2 ZnSnS4 solar cells

In this work, we exploit temperature-light-dependent current-density-voltage (T-JV) and transient photovoltage measurements (T-TPV) to investigate charge dynamics, especially at the back contact, in solution-processed Cu_2 ZnSnS_4 solar cells. A Si_x N _y hole barrier was grown on top of Mo to help to investigate carrier dynamics. By using T-JV techniques, we are able to observe the dominant recombination mechanism occurring at the back contact interface that could lead to significant open-circuit voltage $(V_{oc})$ loss. In combination with T-TPV, TPV decay time mapping across temperature in a range of 213-313 K and light intensity range of 0.01-1 suns was used to explore interface related recombination and charge transport for CZTS solar cell devices.
Bangla Content Categorization Using Text Based Supervised Learning Methods

The widespread and increasing availability of text documents in electronic form increases the importance of using automatic methods to analyze the content of text documents. Specifically, there is a great development in Bangla content generation due to greater number of users in the recent years in social media. In this paper, we present a supervised learning based Bangla content classification method. We have created a large Bangla content dataset and made it available for use publicly. This dataset was tested using several machine learning algorithms using text based features. Our experiments showed logistic regression worked best compared to other algorithms. We have developed an online tool based on our method and made it available for content categorization at: http://samsparkl-00l-sitel.etempurl. com/. We have also made our data extraction tool and the dataset available for use of the other researchers from: https://github.com/sspaarkk/BanglaNLP and also as an web based API from: http://samspark1-001-site1.etempurl. com/CorpusBuilder/.
Residential Advanced Photovoltaic Inverter Pilot Study Results for Select Distribution Secondaries in Hawai‘i

Penetration levels of residential rooftop solar photovoltaic (PV) generation on the Hawaiian island of O`ahu have increased significantly in recent years, with many circuits now at the limits of their PV hosting capacity. NREL and Hawaiian Electric have used this opportunity to deploy advanced inverters at new customer installations that would not otherwise have been approved, as a pilot study to gain first-hand operational field data and experience on the impact of inverter-based grid support functions. At each customer location, the deployed advanced inverters were remotely controlled and monitored. Additionally, customer AMI (smart meter) and secondary distribution transformer data was also available for the analysis. The analysis focused on quantifying the customer behind-the-meter voltage rise due to PV production, determining the customer voltage rise with advanced inverters in unity power factor as well as advanced grid support function modes, and estimating the curtailment when inverters have Volt-VAR and Volt-Watt enabled. The results show that: 1) Behind-the-meter voltage rise is often not negligible. 2) An inverter’s operating point on a Volt-VAR/Volt-Watt curve depends strongly on the design of the secondary system on which it is installed. 3) The mitigation of voltage rise on the secondary system using Volt-VAR may not be effective if only a small proportion of inverters have grid support enabled, or if the reactance/resistance (X/R) ratio of the grid is low. 4) Volt-Watt curtailment can be estimated using co-located irradiance sensors but there may be opportunities to estimate it using inverter internal electrical measurements. 5) Estimated curtailment is very low for the locations analyzed to date.
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. $10^4$ GPU hours) makes it difficult to \emph{directly} search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize~\emph{proxy} tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present \emph{ProxylessNAS} that can \emph{directly} learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08\% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6$\times$ fewer parameters. On ImageNet, our model achieves 3.1\% better top-1 accuracy than MobileNetV2, while being 1.2$\times$ faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for efficient CNN architecture design.
A Neural Control Architecture for Joint-Drift-Free and Fault-Tolerant Redundant Robot Manipulators

Fault tolerance is important for a redundant robot manipulator, which endows the robot with the capability of finishing the end-effector task even when one or some of joints’ motion fails. In this paper, a varying-parameter neural control architecture is designed to achieve fault tolerance for redundant robot manipulators. Specifically, a quadratic programming (QP)-based fault-tolerant motion planning scheme is formulated. Second, a varying parameter recurrent neural network (VP-RNN) is proposed to resolve the standard QP problem, which can make the remaining healthy joints to remedy the whole system which is effected by faulty joints and complete the expected end-effector path. Theoretical analysis based on Lyapunov stability theory proves that the proposed VP-RNN solver can globally converge to the optimal solution to the fault-tolerant motion planning scheme, and the joint motion failure problems are solved successfully. Computer simulations and physical experiments based on a 6 degrees-of-freedom Kinova Jaco2 robot substantiate the effectiveness of the proposed varying-parameter neural control architecture for fault-tolerant motion planning scheme to redundant robot manipulators.
The Effect of Histological Subtypes on Outcomes of Stage IV Epithelial Ovarian Cancer

Introduction: To examined survival outcome by histological subtypes in de novo stage IV epithelial ovarian cancer (EOC). Methods: Between 2004 and 2015, patients with stage IV EOC were included using the Surveillance, Epidemiology, and End Results program. The effects of histological subtypes on overall survival (OS) were assessed using Kaplan–Meier and multivariable Cox regression analyses. Results: We identified 5,953 patients including 5,351 (89.9%), 249 (4.2%), 145 (2.4%), and 208 (3.4%) patients with high-grade serous, endometrioid, mucinous, and clear cell subtypes, respectively. The 5-year OS rates were 28.1, 38.6, 14.2, and 18.8% in patients with high-grade serous, endometrioid, mucinous, and 18.8% clear cell subtypes, respectively, (p < 0.001). Multivariate analyses indicated that histological subtype was an independent predictor of OS. Using the high-grade serous subtype as a reference, OS was comparable for the endometrioid subtype (hazard ratio (HR) 0.915, 95% confidence interval) (CI 0.772–1.085, p = 0.305), but significantly lower for mucinous (HR 3.292, 95% CI 2.701–4.011, p < 0.001) and clear cell subtypes (HR 1.820, 95% CI 1.546–2.141, p < 0.001). Patients with no residual tumor had better OS in the high-grade serous and endometrioid subtypes compared to patients with residual tumors. However, the residual tumor size was not a prognostic factor for OS in mucinous and clear cell carcinoma. Conclusions: Our study suggest a markedly mortality rate in patients with stage IV mucinous and clear cell carcinoma, but better survival in patients with high-grade serous and endometrioid subtypes. Aggressive radical surgery to leave no residual disease would improve survival for high-grade serous and endometrioid carcinoma. More studies are needed to assess the value of aggressive radical surgery in patients with mucinous and clear cell subtypes.
High-Gain Dead-Zone Observers for Linear and Nonlinear Plants

We propose an adaptive dead-zone mechanism to robustify observers against high-frequency noise. The construction applies to Luenberger observers and high-gain observers for plants in strict feedback form. The dead-zone improves performances by trimming a portion of the output injection term and trapping the high frequency noise in the dead band. We show that the observer gain and the adaptation parameters can be obtained by solving a linear matrix inequality, whose feasibility only requires detectability of the plant. The parameters obtained through this optimization ensure (in the absence of noise) global exponential stability of the estimation error dynamics, and input-to-state stability from the measurement noise to the estimation error.
Analysis and Control Considerations of an Open Winding IPMSM Drive in MTPA and FW Regions

Electromagnetic performance and expected operating capabilities of a conventional single inverter Interior Permanent Magnet Synchronous Motor (IPMSM) drive are evaluated and compared with dual inverter open winding IPMSM drives in Maximum Torque per Ampere (MTPA) and Field Weakening (FW) regions considering machine's nonlinearities and losses. Different real and reactive power sharing assumptions are made in the dual inverter drives, and it is shown how each of the related controllers is accordingly defined. It is demonstrated that in certain operating regions of the open winding machine's torque versus speed envelope, the original real and reactive power sharing principles and the defined controllers have to be modified, otherwise the machine may not be able to operate throughout its entire anticipated operating points.
Agility in the Balance: Control, Autonomy, and Ambidexterity in Agile Software Development

Agile methodologies for information systems development (ISD) are still drawing the attention of the research community. These methodologies promise to increase an ISD team’s adaptiveness in such a way that ISD teams are able to respond and react to changing user requirements.Existing studies on team autonomy in agile ISD, however, implythat these projects potentially can benefit from different elements of control.Our objective is to improve the understanding of how to enact control throughagilepractices, and how these practices influenceteam autonomy and task performance in successful agile ISD projectsin terms of project performance and project quality. This is achieved by developing a preliminary research model that is based on a solid theoretical foundation. As a theoretical framework, we employ ISD ambidexterityand extend it with context-specific insights from controltheory. In consequence, we suggest several propositions for future testing.
An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks

Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area.
Output Injection Filtering Redesign in High-Gain Observers

We propose a new paradigm to redesign high-gain observers in order to improve performances in the presence of measurement noise. In particular, instead of driving the observer by means of a standard output injection term, we filter it with a dynamical system having good filtering properties. In this first preliminary result we also select the filter in order to address numerical challenges.
Real-Time Wireless Vibration Monitoring Using SAW RFID Tags Coupled with Sensors

This work addresses the challenge of simultaneous wireless identification and real-time wideband vibration measurement using a SAW (Surface Acoustic Wave) device. Vibration of rotating machinery is imprinted in the SAW ID tag response via modulation of the antenna feed impedance using external sensors. The key component at signal processing side is low-latency preprocessing to ensure that vibration information is acquired unambiguously where the system concept basically allows the measurement of frequencies from zero up to several hundred kilohertz. Feasibility is demonstrated based on real-world measurements of healthy and faulty rotating fans.
Traffic Sign Image Synthesis with Generative Adversarial Networks

Deep convolutional neural networks (CNN) has achieved state-of-the-art result on traffic sign classification, which plays a key role in intelligent transportation system. However, it usually requires a large number of labeled training data, which is not always available, to guarantee a good performance. In this paper, we propose to synthesize traffic sign images by generative adversarial networks (GANs). It takes a standard traffic sign template and a background image as input to the generative network in GANs, where the template defines which class of traffic sign to include and the background image controls the visual appearance of the synthetic images. Experiments show that our method could generate more realistic traffic sign images than the conventional image synthesis method. Meanwhile, by adding the synthesis images to train a typical CNN for traffic sign classification, we obtained a better accuracy.
Training Flownet on Monkaa Dataset

Optical flow is a typical ill-posed problem which has benefited a lot from recent advances in deep learning. In this manuscript, we discuss training issues and solution for training Flownet architecture for the optical flow problem on the Monkaa Dataset. We also compare the results with Flying Chairs dataset training results. We further discuss new ideas and future work direction.
Spatial-Temporal Distance Metric Embedding for Time-Specific POI Recommendation

With the growing popularity of location-based social networks (LBSNs), time-specific POI recommendation has become important in recent years, which provides more accurate recommendation services for users in specific spatio–temporal contexts. In this paper, we propose a spatio–temporal distance metric embedding model (ST-DME) for time–specific recommendation, which exploits both temporal and geo-sequential property of a check-in to effectively model users’ time-specific preferences. Specifically, we divide timestamps of user’ check-ins into different time slots and adopt Euclidean distance rather than inner product of latent vectors to measure users’ preferences for POIs in a given time slot. We also apply a transition coefficient based on users’ most recent check-ins to incorporate geo-sequential influence in users’ check-in behaviors. A weighted pairwise loss with a hard sampling strategy is adopted to optimize latent vectors of users, POIs, and time slots in a metric space. Extensive experiments are conducted to demonstrate the effectiveness of our proposed method and results show that ST-DME outperforms state-of-the-art algorithms for time-specific POI recommendation on two public LBSNs data sets.
Modeling of Process (Ge, N) Dependence and Mechanical Strain Impact on NBTI in HKMG SiGe GF FDSOI p-MOSFETs and RMG p-FinFETs

A physical framework is used to model time kinetics of Negative Bias Temperature Instability (NBTI) in Si and SiGe FDSOI p-MOSFETs and p-FinFETs. The effects of Germanium (Ge%) in the channel and Nitrogen (N%) in the High-K Metal Gate (HKMG) gate stack are explained. Mechanical strain effects in terms of STI to active distance (SA) for FDSOI and channel length (L) scaling for FinFET are explained. Band structure is calculated to correlate the process (Ge%, N%, strain) impact on device degradation. The model is included in Sentaurus Device TCAD to predict NBTI kinetics in Si and SiGe FinFETs.
To Rate or to Fund? - The Effect of Idea Evaluation Platform Design on Decision Quality and User Engagement

Given the importance of scalable and real-time idea evaluation for commercial success and survival, organizations increasingly draw on the principle of the wisdom of crowds. This principle is commonly being operationalized in platforms that involve customers or employees in the task of idea evaluation. However, there is still a lack of scientific evidence on how the design of such platforms can contribute to the assessment of idea quality, while providing users with a meaningful and enjoyable experience. This study elaborates on two characteristic design components of open idea evaluation platforms: the selection of an evaluation mechanism (rating scales vs. crowdfunding) and the availability of peer ratings. We conduct a multi-method study including a web-based experiment with 202 participants evaluating ideas in the context of electric mobility, a survey capturing user experience, and an expert focus group that provides an elaborate ranking of the ideas. Results indicate that crowdfunding – despite its rising popularity – and peer rating availability – despite often chosen as a default option – negatively impact decision quality, while not increasing user engagement. This research-in-progress paper contributes to research on both collective intelligence and innovation management, by investigating how different design components of idea evaluation platforms translate into differences concerning decision quality and user engagement.
Privacy Preserving Dynamically Indexed Multi-Phrase Search over Encrypted Data

With the widespread usage of Cloud Computing model in business, organizations prefer to outsource their data to the cloud. In order to preserve confidentiality of data, the data is encrypted before being outsourced. In order to use this encrypted data in decision making, symmetric searchable encryption is employed that enables users to fetch data matching the keywords over encrypted data without decrypting the data. Many existing schemes search for a single keyword without an index, leading to search time linear to the size of data. And moreover, support for multiple keyword searches or phrase search is also a trending challenge in the field of searchable encryption. In this paper, we extend the practical techniques for searches on encrypted data proposed by Song, et.al, by improving the search efficiency with help of an encrypted and dynamic index. We use a very efficient structure to enable dynamic updates to the index in event of changes to the encrypted dataset. Our proposed scheme is the first one to use such a simple yet update-efficient structure for index. Multi-phrase search being implemented helps in obtaining most representative documents for searches, especially in mobile devices. We also implemented and analyzed efficiency of Song et al scheme, which is one of the pioneering works in field of searchable encryption. Security analysis and extensive experiments were conducted to test the security guarantees and efficiency of the proposed extensions.
Enhanced Regeneration and Hepatoprotective Effects of Interleukin 22 Fusion Protein on a Predamaged Liver Undergoing Partial Hepatectomy

Liver ischemia-reperfusion injury (IRI) and regeneration deficiency are two major challenges for surgery patients with chronic liver disease. As a survival factor for hepatocytes, interleukin 22 (IL-22) plays an important role in hepatoprotection and the promotion of regeneration after hepatectomy. In this study, we aim to investigate the roles of an interleukin 22 fusion protein (IL-22-FP) in mice with a predamaged liver after a two-third partial hepatectomy (PHx). Predamaged livers in mice were induced by concanavalin A (ConA)/carbon tetrachloride (CCl4) following PHx with or without IL-22-FP treatment. A hepatic IRI mouse model was also used to determine the hepatoprotective effects of IL-22-FP. In the ConA/CCl4 model, IL-22-FP treatment alleviated liver injury and accelerated hepatocyte proliferation. Administration of IL-22-FP activated the hepatic signal transducer and activator of transcription 3 (STAT3) and upregulated the expression of many mitogenic proteins. IL-22-FP treatment prior to IRI effectively reduced liver damage through decreased aminotransferase and improved liver histology. In conclusion, IL-22-FP promotes liver regeneration in mice with predamaged livers following PHx and alleviates IRI-induced liver injury. Our study suggests that IL-22-FP may represent a promising therapeutic drug against regeneration deficiency and liver IRI in patients who have undergone PHx.