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This issue of the IEEE Journal of Solid-State Circuits (JSSC) includes some of the highlights of the best papers from the 2018 Asian Solid-State Circuits Conference (A-SSCC), which was held at the Shangri-La’s Far Eastern Plaza Hotel, Tainan, Taiwan, on November 5–7, 2018. As one of the five conferences fully sponsored by the IEEE Solid-State Circuits Society, the A-SSCC was in its 14th appearance in 2018 since its inauguration in 2005 in Taiwan. Rotating among Asian countries, the A-SSCC is quickly becoming one of the leading global forums for the presentation of advances in solid-state circuits and system-on-a-chip (SOC). From 2018, this conference accepts field-programmable gate array (FPGA) papers for presentation and demo and allows two-page format papers for submission and publication. [ABSTRACT FROM AUTHOR]

This Special Issue of the IEEE Journal of Solid-State Circuits (JSSC) includes some of the highlights of the best papers presented at the 2020 Asian Solid-State Circuits Conference (A-SSCC), which was held on November 9–11, 2020. A-SSCC 2020 was originally planned to be held in Hiroshima, Japan. However, due to COVID-19, A-SSCC 2020 had to become a virtual conference. As one of the five conferences fully sponsored by the IEEE Solid-State Circuits Society, the A-SSCC was in its 16th appearance in 2020 since its inauguration in 2005 in Taiwan. Rotating among Asian countries, the A-SSCC is quickly becoming one of the leading global forums for the presentation of advances in solid-state circuits and system-on-a-chip (SoC). From 2018, this conference accepts field-programmable gate array (FPGA) papers for presentation and demo and allows two-page format papers for submission and publication. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing interest in open government data (OGD) around the world. The governments of various countries have encouraged firms to adopt OGD to develop innovative products and services to bring about social and economic benefits. This paper examines the factors influencing the adoption of OGD among firms in Taiwan to fill a gap that prior studies do not adequately address. Based on the sociotechnical perspective, we developed a research model that integrates the following factors that are central to adoption decisions of firms: top management support, government support, competitive pressure, perceived benefits, and perceived barriers. We examined this model through survey data from firms with experience in OGD adoption in Taiwan. The results show a significant positive relationship among top management support, competitive pressure, and the adoption of OGD by firms. This paper serves as a first attempt to contribute to advancing knowledge on OGD adoption at the firm level via empirically testing a research model that integrates factors within social and technical systems. This paper has significant implications for practitioners of enterprise information management and creates a valuable reference for other countries in the early stages of OGD initiatives. [ABSTRACT FROM AUTHOR]

It is vital to have an exclusive modification in semiconductor production process because of meeting differentiated customer demands in dynamic and competitive global minuscule semiconductor technology market and the highly complex fabrication process. In this paper, we propose a control system based on the dynamic mixed-effect least-square support vector regression (LS-SVR) control system for overlay error compensation with stochastic metrology delay to minimize the misalignment of the patterning process. Moreover, for the stability of the control system in the presence of metrology delay and to deal with nonlinearity among the overlay factors, the novel Lyapunov-based kernel function is merged with the LS-SVR controller. The proposed controller’s operation has been validated and implemented by a major semiconductor manufacturer in Taiwan. The experiments are verified that mixed-effect LS-SVR controller has the higher validity and higher efficiency in comparison with the exponentially weighted moving average (EWMA) and threaded EWMA controllers which had been previously implemented at the company or applied in similar studies. Note to Practitioners—Due to high production complexity in semiconductor manufacturing process, a meticulous and intelligent process control is needed to achieve higher throughput and customer satisfaction. Monitoring a complex system is challenging because the process components and variables operate autonomously and interoperate with other manufacturing segments. This paper proposes a novel run-to-run (R2R) control system to compensate the overlay error during the photolithography process that efficiently deals with the high-mixed manufacturing environment and metrology delay. [ABSTRACT FROM AUTHOR]

The concept of the smart city has been created in response to the increasing numbers of people living in cities. Just as the development of technology has evolved rapidly, smart life has also emerged. The goal of the smart city is one day to enable all affairs of daily life to be completed with the single touch of a finger through cutting-edge technology. New and innovative information must be applied effectively to the industry. With the industrial revolution, propelled by the Internet of Things (IoT), big data and the cloud platform have birthed the “smart city,” integrating the IoT and the cloud through mobile devices and applying technology to fields like logistics, finance, healthcare, recreation, surveillance, and traffic transportation, thus providing people with greater well-being and convenience. Following the era of the big data knowledge economy, the IoT has become an important pillar of national economic development. The IoT is expected to provide substantial support for continued and sustainable development of the smart city. Therefore, effective use of the IoT has become an important topic in smart city development. The purpose of this paper is to build a conceptual framework of service innovation, relying on the smart city case of Taiwan. Based on a literature review, in-depth interviews, and case interviews, the proposed conceptual framework of IoT is rooted in the market, policy, and technical aspects. It incorporates eight steps: composition of the project team, service idea generation, service idea screening, development concept selection, design and development, service testing, commercialization, and service quality, to generate new service value and thereby create customer satisfaction. In fact, the IoT is designed to support the smart city vision; thus, this paper describes the various innovation modes of the smart city. Furthermore, the paper presents and discusses the technical solutions and best-practice guidelines adopted in the Taiwan Smart City project. Finally, it discusses the meaning and future research direction of the smart city through the use of the IoT. With wave after wave of digital development civilizing the city's evolution and with the phenomenon of Internet access through mobile phones for each person, the smart city of human factors is clearly coming. [ABSTRACT FROM AUTHOR]

The Special Issue of the IEEE Journal of Solid- State Circuits (JSSC) includes some of the highlights of the best papers presented at the 2021 Asian Solid-State Circuits Conference (A-SSCC). This year, the A-SSCC 2021 is held as a hybrid conference on November 7–10, 2021 with attendees having the option to join the in-person meeting in Busan, Korea, or participate virtually through on-line platform. As one of the five conferences fully sponsored by the IEEE Solid-State Circuits Society, the A-SSCC was in its 17th appearance in 2021 since its inauguration in 2005 in Taiwan. Rotating among Asian countries, the A-SSCC is quickly becoming one of the leading global forums for the presentation of advances in solid-state circuits and system-on-a-chip (SoC). From 2021, only two-page format articles are allowed for submission and publication. [ABSTRACT FROM AUTHOR]

A fast retransmit, which reduces the sender’s waiting time before retransmitting a lost segment, is applied to guarantee data integrity with no data loss in transmission. Based on fast retransmit, many application protocols have been enhanced and evolved to ensure quality of service and reduce data transmission time. One of application protocols is the multipath transmission control protocol which is catholically applied in modern computer networks. Communication lines used in this kind of network have different states, namely, failure, partial failure, and maintenance. Therefore, a computer network with a fast retransmit can be classified as stochastic and is called a stochastic-flow computer network with a fast retransmit (SCNFR). This paper assesses the system reliability of SCNFR for the successful transmission of demand d through ${k}$ (${k} \,\, >1$) separate minimal paths (${k}$ SMiP) under the latency. An algorithm is proposed to find all minimal capacity vectors (MCVs) that satisfy the demand and latency. Then, the system reliability is computed based on all MCVs. We adopt two practical cases of the pan-European research and education network and the Taiwan academic network to explore the effectiveness of the algorithm. The results show that the system reliability can be classified as decision reference while the manager decides the better ${k}$ SMiP with higher system reliability. [ABSTRACT FROM AUTHOR]

Submarine cables are indispensable for offshore wind farms (OWFs) connected to a power grid. The submarine cable can cause certain degrees of impact on the system performance due to its different lengths, characteristics, electrical parameters, etc. This paper employs the power-system simulation software of Power System Simulator for Engineering (PSS/E) for modeling a future-scheduled OWF, i.e., a large-scale doubly-fed induction generator-based OWF of 200 MW, connected to Jang-Bin distribution substation of Taiwan Power System through five feeders, five circuit breakers, two step-up power transformers, a commercial submarine cable, and an underground cable. This paper simulates and compares the electrical quantities of the studied OWF with different operating capacities under various lengths of three types of commercial submarine cable. The transient surge peak voltages at the common bus of the OWF and the bus of Jang-Bin distribution substation subject to the switching of one of the five circuit breakers of the studied OWF under different lengths of the three types of commercial submarine cable are also compared using the power-system simulation software of ATPDraw in Alternative Transients Program (ATP) version of Electromagnetic Transients Program (EMTP). [ABSTRACT FROM AUTHOR]

The displacement of synchronous generators by converter interfaced energy renewable resource essentially reduces the system inertia, which can trigger under-frequency load shedding relay. To support the frequency containment, virtual inertia control (VIC) and fast frequency control (FFC) based on energy storage system (ESS) have been developed. Most of the existing VIC did not take into account the power constraints of the ESS. Although some of the FFC methods account for the ESS constraints, they ignore the intermediate steady-state (ISS) operating point of the primary frequency response (PFR). To counteract these problems, a model predictive control (MPC) based FFC, which considers the power limitations of the ESS, the frequency deviation constraint, and the ISS frequency tracking capability, is proposed in this paper. The proposed MPC only requires few local measurements, and the system parameters including inertia constants, onset time of inertia response and the ISS time of the PFR can be accurately estimated. To achieve the optimal control action and reduce computational burden, quadratic programming (QP) is incorporated in the proposed MPC. The proposed MPC is tested on a real Taiwan power system. The fast response and robustness of the proposed MPC has been justified by comparing with the state-of-the-art method. [ABSTRACT FROM AUTHOR]

If an extreme contingency strikes the Kinmen Island power system in Taiwan, it is very likely to cause blackouts. Although an underfrequency protection scheme has been applied, some drawbacks of the protection scheme hinder the Kinmen power system from avoiding blackouts. In the past decade, several blackouts have occurred in the Kinmen power system. To avoid further possible blackouts, a special protection system (SPS) is summoned. This paper describes the design guidelines and deployment procedures of an SPS for the Kinmen power system. The design depends on the Kinmen power system characteristics, and the deployment hinges upon the supervisory control and data acquisition system. Exquisite software was also developed to testify the Kinmen system SPS. Operational records have demonstrated that the proposed SPS can indeed prevent blackouts from occurring in the Kinmen power system. The design, implementation, and operation of the SPS dealt with in this paper might foster further deployments of other SPSs. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and field measurements of a prototype marine-current power generation system (MCPGS) for capturing energy from marine current. The studied prototype MCPGS employs the flowing marine-current energy near the Cross-Sea Bridge, Peng-Hu Island, Taiwan. The design of the prototype MCPGS has to meet the practical marine-current flowing conditions and the severe environmental conditions in the ocean of Peng-Hu Island. The designed MCPGS unit consists of a three-blade marine-current turbine, a gearbox, a three-phase permanent-magnet generator, and a power converter that is used for converting generated energy to be stored in the battery. Field measured results for the studied MCPGS under different practical marine-current speeds in Peng-Hu Island are performed and analyzed. [ABSTRACT FROM AUTHOR]

In a power system, the transmission network is the backbone of electricity delivery from power plants to load centers; hence, it becomes a critical issue for electrical engineers to shorten the service restoration time after a fault occurrence on the transmission line. This application paper integrates the two- and three-terminal hybrid transmission line fault location algorithms based on synchronized phasor measurements into the proposed cloud-computing-based fault location system to inspect the feasibility and practicality of our algorithms for the real transmission-line incidents. The computing results are performed via Google Map for authorized personal computers and the application for intelligent mobile devices is developed to assist engineers in acquiring the fault-event information immediately as well as in figuring out the fault position intuitively and quickly due to its visualization and navigation. Last but not the least, the suggested fault location system has already been applied to the practical 345- and 161-kV transmission networks of Taiwan Power Company for achieving the combination of theory and practice. Up to the present, the proposed system still yields excellent performance verified by realistic fault data. [ABSTRACT FROM AUTHOR]

An optimal planning model of the sectional switch and tie line for the distribution network is proposed considering the tradeoff between the system reliability and economy. First, the fault incidence matrices are established for the distribution network, and the explicit analytical expression of the system reliability index is realized by the algebraic operation of the fault incidence matrices and fault parameter vectors. Then, the 0–1 integer quadratically constrained optimization model is established for the sectional switch configuration considering the capacity constraint of the tie line. Finally, the sectional switches and tie lines are optimized jointly to ensure the economic investment and maximize the system reliability. The distribution network in Taiwan Power Company is used to verify the applicability of the proposed optimization method. Compared with the intelligent optimization algorithm, the mathematical programming method in this paper can always find the global optimal switch configuration quickly. In addition, the influence of the tie line capacity constraints on the sectional switch configuration is also considered, which can provide practical reference for the power network planners in the optimization design of the distribution network. [ABSTRACT FROM AUTHOR]

The annual output value of Taiwan’s wafer foundry and IC packaging test ranks first worldwide. The electronics industry has a complete industrial ecological chain in the supply chain system of global information and communication technology. In order to increase market competitiveness and increase the operational flexibility, manufacturing process outsourcing has become a trend in the business model of the electronics industry. The quality of the outsourcing affects the quality and function of the final product, the outsourcer selection is crucial to ensure the quality of the final product. This article takes the wire bonding process as an example, and proposes a fuzzy selection model of quality-based IC packaging process outsourcers. Then, this article selects the six standard deviation indicator that can fully reflect the process yield and quality level as the evaluation tool. Since the indicator contains unknown parameter, in order to improve the accuracy of estimation and overcome the uncertainty of measurement data, this paper applies mathematical programming to derive the confidence interval of six sigma quality index, and proposes a confidence-interval-based fuzzy testing meth as a green outsourcer selection tool to reduce the risk of misjudgment due to sampling errors and improve the accuracy of the selection. [ABSTRACT FROM AUTHOR]

The article presents information on the 39th Annual International Computer, Software & Applications Conference to be held in July 1-5, 2015 in Taiwan.

Most catch up studies focus on “external” foreign sources of technological learning as a means of building up latecomer technological capability. By contrast, strategic management studies in advanced countries center on the “internal” sources of organizational learning and routines (including R&D) within a firm, aimed at developing and renewing its knowledge and its absorptive capacity. This paper draws from both groups of studies to examine how latecomer firms accumulate in-house knowledge bases and assimilate foreign knowledge to create strategic innovation capabilities. We propose a clear three-stage development framework to examine two leading firms, AU Optronics (AUO) and Chimei Innolux (CMI), in Taiwan's flat panel display industry. This paper shows the coevolution of the firms’ knowledge bases and external foreign sources of knowledge over time. We find that, with their increasing knowledge bases and strategic advancement over time, the firms are able to benefit from knowledge transfer; to build up their high-level innovative capabilities, and in the process to move from junior to senior status with foreign firms. These developments appear to allow leading firms to move “across” the technological frontier, contributing to a strengthening competitive position in the international market. [ABSTRACT FROM PUBLISHER]

In the semiconductor industry, the testing section has always played an important role. The testing section often requires engineers to judge the defect, which wastes a lot of time and cost. The accurate classification can provide useful information for engineers through neural networks. In this paper, we present a method for wafer map data augmentation and defect classification. Data augmentation is based on CNN encoder-decoder and the classification is based on depthwise separable convolutions. There are two datasets used, one is open dataset WM-811K and the other is built with a Taiwan company. We train two models with mobilenetV1 and V2 for the different datasets. The light-weight deep convolution can reduce model parameters and calculations, which is very efficient for the testing house with large production volumes. On two different data sets, our proposed method can reduce the number of parameters by 30% and 95%, and reduce the amount of calculation by 75% and 25%, respectively. The test accuracy of the first dataset is 93.95%. The second dataset test accuracy is 87.04%. After the data augmentation, accuracy is increased to 97.01% and 95.09%, respectively. [ABSTRACT FROM AUTHOR]

Variations of the ionosphere can significantly disrupt synthetic aperture radar (SAR) acquisitions and interferometric measurements of ground deformation. In this paper, we show how the ionosphere can also strongly modify C-band interferograms despite its smaller influence at higher frequencies. Thus, ionospheric phase screens should not be neglected: their compensation improves the estimation of ground deformation maps. The split-spectrum method is able to estimate the dispersive ionospheric component of the interferometric phase; we describe the implementation of this method for the burst modes TOPS and ScanSAR to estimate and remove ionospheric phase screens. We present Sentinel-1 interferograms of the 2016 Taiwan earthquake and ALOS-2 interferograms of the 2015 Nepal earthquake, which show strong ionospheric phase gradients, and their corrected versions. Finally, to validate the results and better understand the origin of these ionospheric variations, we compare the estimated differential ionosphere with global Total Electron Content maps and local Global Positioning System measurements. [ABSTRACT FROM PUBLISHER]

In urban environments, Global Positioning System (GPS) signals are often reflected or blocked by buildings causing multipath effects and Non-Line-Of-Sight (NLOS) reception. These effects degrade GPS positioning performance. Although improved receiver and antenna design can reduce the multipath effect to some extent, NLOS elimination is limited due to sensor cost. Measurements based modelling methods have shown promise to reduce NLOS, while positioning accuracy is limited by the correct classification of the signal reception types. These limitations are addressed in this paper by developing an integrated algorithm using matching, Particle Swam Optimized Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and a spatial city model. The user's location is determined by matching the high accuracy signal reception type determined by PSO-ANFIS with satellite visibility of each candidate position determined by the ray-tracing and a 3D city model. The results from a field test in an urban area in Taiwan show that the PSO-ANFIS based algorithm achieves a classification accuracy of 96%, 88%, and 91% for LOS, multipath and NLOS signals, respectively, which is superior to the other classification algorithms. The final horizontal positioning accuracies in terms of the Root Mean Square Error (RMSE) are 3.20 m using a commercial receiver and 1.88 m using a geodetic receiver. The proposed algorithm has consistent improvements of 88% and 70% respectively compared to the traditional Weighted Least Square (WLS) based positioning method, and is the best compared to the other state-of-the art methods with the improvements ranging from 15% to 77% depending on the test conditions. [ABSTRACT FROM AUTHOR]

Earthquake early warning system uses high-speed computer network to transmit earthquake information to population center ahead of the arrival of destructive earthquake waves. This short (10 s of seconds) lead time will allow emergency responses such as turning off gas pipeline valves to be activated to mitigate potential disaster and casualties. However, the excessive false alarm rate of such a system imposes heavy cost in terms of loss of services, undue panics, and diminishing credibility of such a warning system. At the current, the decision algorithm to issue an early warning of the onset of an earthquake is often based on empirically chosen features and heuristically set thresholds and suffers from excessive false alarm rate. In this paper, we experimented with three advanced machine learning algorithms, namely, $K$ -nearest neighbor (KNN), classification tree, and support vector machine (SVM) and compared their performance against a traditional criterion-based method. Using the seismic data collected by an experimental strong motion detection network in Taiwan for these experiments, we observed that the machine learning algorithms exhibit higher detection accuracy with much reduced false alarm rate. [ABSTRACT FROM AUTHOR]

In the semiconductor manufacturing industry the development of single index assessing the equipment health condition is urgent, and thus the dashboard can be used for monitoring thousands of equipment. This paper proposes a novel equipment health index (EHI) framework and a Hotelling T-squared index (HTI) to monitor equipment and support preventive maintenance in real time in the semiconductor manufacturing industry. The EHI framework consists of data preprocessing, statistical process control, analytic hierarchy process, and a comprehensive single index representing the health condition of equipment. It considers different types of the univariate control charts for each status variable identification (SVID), and aggregates the scores corresponding to the control charts throughout the hierarchical structure. The HTI considers the correlation among the selected SVIDs and builds a multivariate index by using Hotelling T-squared statistic. An empirical study of Taiwan’s leading semiconductor assembly manufacturer finds that both the EHI and the HTI supported monitoring thousands of pieces of equipment in real time. In practice, firms can rapidly troubleshoot the root causes of failures by the decomposition of EHI. [ABSTRACT FROM AUTHOR]

It is our privilege to present selected papers from the Asia-Pacific Data Storage Conference 2013 (APDSC’13), held from November 20-22, 2013 in Hualien, Taiwan. APDSC’13 was hosted by the Taiwan Information Storage Association, the National Dong Hwa University, and the Electronics and Optoelectronics Research Laboratories, ITRI, Taiwan. The Conference was also sponsored by the following organizations: The National Science Council, The Ministry of Education, and The Ministry of Economic Affairs of Taiwan, and the IEEE Magnetics Society, The Optical Society of America, The Society of Information Storage System, Korea, and International Society for Optical Engineering, The Magnetics Society of Japan and The Korean Magnetics Society. [ABSTRACT FROM PUBLISHER]

Railway rolling stock is one of the most expensive assets of railway operators. Efficient utilization of rolling stock is one of the most important objectives pursued in practice. This research focuses on the improvement in rolling stock rostering efficiency for high-speed rail system. According to the circulation schedule and long-term maintenance plan, the rostering planner decides the assignment between rolling stock and duties subject to a set of practical constraints. Owing to its complexity, this task remains a manual process at the Taiwan High Speed Rail Corporation. Experienced railway practitioners can generally create a good and feasible plan, but they cannot guarantee optimality of the solution considering only short-term process. In this research, we developed an exact optimization model and a heuristic method to automate this process and improve the efficiency of rolling stock utilization. Results from the case studies demonstrate that the efficiency of the rolling stock usage can be increased by 3\%\sim 4\%, and that the process time can be significantly reduced from hours to seconds. [ABSTRACT FROM AUTHOR]

As the penetration of photovoltaic (PV) solar generation has increased, a significant number of residential and commercial solar PV systems have been installed without meters. The majority of these systems, however, are also not monitored by power system operators. Therefore, the uncertainty of the net load owing to this “invisible” solar power generation has raised additional challenges for power system operation. To reduce the impact of this, we propose a novel method to estimate the total solar power generation in a large region from a small representative subset. The proposed method is capable of capturing all relevant information that could assist in the identification of the representative subsets. Moreover, different optimization algorithms are utilized and evaluated to select the optimal number of clusters and representative subsets. As a case study, the power generation of 166 PV sites in Taiwan was collected and analyzed. The proposed method demonstrates a significant improvement in estimating aggregated power generation compared to other existing methods. [ABSTRACT FROM AUTHOR]

The progress of industrial development naturally leads to the demand for more electrical power. Unfortunately, due to the fear of the safety of nuclear power plants, many countries have relied on thermal power plants, which will cause more air pollutants during the process of coal burning. This phenomenon as well as increased vehicle emissions around us, have constituted the primary factors of serious air pollution. Inhaling too much particulate air pollution may lead to respiratory diseases and even death, especially PM $_{2.5}$ 2. 5 . By predicting the air pollutant concentration, people can take precautions to avoid overexposure to air pollutants. Consequently, accurate PM $_{2.5}$ 2. 5 prediction becomes more important. In this study, we propose a PM $_{2.5}$ 2. 5 prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. Autoencoder and Linear interpolation are adopted for solving the missing value problem. Spearman’s correlation coefficient is used to identify the most relevant features for PM $_{2.5}$ 2. 5 . Two prediction models (i.e., LSTM and LSTM based on K-means) are implemented which predict PM $_{2.5}$ 2. 5 value for each Airbox device. To assess the performance of the model prediction, the daily average error and the hourly average accuracy for the duration of a week are calculated. The experimental results show that LSTM based on K-means has the best performance among all methods. Therefore, LSTM based on K-means is chosen to provide real-time PM $_{2.5}$ 2. 5 prediction through the Linebot. [ABSTRACT FROM AUTHOR]

A study of sound propagation over a submarine canyon northeast of Taiwan was made using mobile acoustic sources during a joint ocean acoustic and physical oceanographic experiment in 2009. The acoustic signal levels (equivalently, transmission losses) are reported here, and numerical models of 3-D sound propagation are employed to explain the underlying physics. The data show a significant decrease in sound intensity as the source crossed over the canyon, and the numerical model provides a physical insight into this effect. In addition, the model also suggests that reflection from the canyon seabed causes 3-D sound focusing when the direction of propagation is along the canyon axis, which remains to be validated in a future experiment. Environmental uncertainties of water sound speed, bottom geoacoustic properties, and bathymetry are addressed, and the implications for sound propagation prediction in a complex submarine canyon environment are also discussed. [ABSTRACT FROM AUTHOR]

In this study, a forecast system for tsunami early warning in Taiwan is constructed by integrating the source model that generates the initial tsunami elevation based on earthquake fault parameters, the propagation model that calculates the offshore sea surface quickly by precomputed reciprocal Green's functions, and the nearshore module that interpolates the inundated distance to the shoreline based on precomputed inundation simulations. This system is integrated and executed by a Matlab graphic utility interface (GUI). In this interface system, the rupture parameters such as epicenter location, focal depth, and fault angles are used to compute the initial tsunami wave elevation. These rupture parameters can be retrieved from the website of the U.S. Geological Survey or the F-net Broadband Seismograph Network of Japan for the most updated earthquake parameters. For the highly nonlinear coastal inundation, a bell-shaped wave is used to generate the inundation map, which is then transformed into a Google Earth format and demonstrated via this geographic information system. This system has been applied in both 2010 Chilean Tsunami and 2011 Great Eastern Japan Tsunami. In both cases, the result has order-of-magnitude accuracy; thus, this system provides a quick first estimation for an early warning of a tsunami when a submarine earthquake occurs. [ABSTRACT FROM PUBLISHER]

Acoustic reflection coefficients and attenuation rolloff rates of the seabed near North Mien-Hua Canyon, northeast of Taiwan, were measured from chirp sonar echoes during the Quantifying, Predicting, and Exploiting (QPE) Uncertainty Initiative Experiment in 2009. Using these measurements and the Biot theory with a fluid approximation, the depth-averaged sound speed, density, and medium attenuation of the surficial sediment layer were estimated. The sediment types in the chirp sonar survey area vary from fine sand to coarse silt. To capture this spatial variability, the 1-D geoacoustic parameter estimates along the chirp sonar track were interpolated onto a 2-D grid using an objective mapping technique. The 2-D maps of surficial sediment properties, along with interpolation errors, can be further applied to underwater sound propagation models in the experimental area. [ABSTRACT FROM AUTHOR]

Fault detection and classification has been employed to enhance yield and product quality for smart semiconductor manufacturing. For early detection of abnormal events that cause defects, the status variables identification data collected by the sensors embedded in advanced machines can be analyzed to derive the actions for advanced process control and advanced equipment control. However, the validity and effectiveness of fault detection and classification technologies may highly depend on domain knowledge and experience of the process engineers who should redefine the monitoring rules quickly when new process excursion occurred especially when ramping up new technologies and products. Motivated by realistic needs, this study aims to propose a novel strategy to empower intelligent fault detection and classification that employed convolutional neural network to analyze the feature SVID data and determine the conditions of the wafers, while shorten the cycle time for self-learning from domain knowledge and redefining new monitoring rules for fault classification in real time. This approach is validated with an empirical study in a leading semiconductor manufacturing company in Taiwan. The results have demonstrated the practical viability of the developed solution. [ABSTRACT FROM AUTHOR]

Manual titration of positive airway pressure (PAP) is a gold standard to provide an optimal pressure for the treatment of obstructive sleep apnea-hypopnea syndrome (OSAS). Since manual titration studies were costly and time-consuming, many statistical models for predicting effective PAPs were reported. However, the prediction accuracies of the models associated with nocturnal parameters still remain low. This study proposes a fuzzy neural prediction network (FNPN) with input candidate variables, selected among easily available measurements (e.g., body mass index (BMI), waist circumstance (WC), and body composition) and OSAS related questionnaires, to rapidly predict an optimal PAP. The FNPN comprises fuzzy rules and is characterized with the ability of automatic rule growing and pruning from training data. A total of 147 participants from April 2018 to April 2019 were enrolled in Taichung Veterans General Hospital, Taiwan. After two selection processes for feature extraction, WC and BMI were the significant variables for entering the FNPN to predict optimal PAP. Experimental results showed that the average successful prediction rate of the proposed method was 71.8%. This study also found that Epworth sleepiness scales (ESS) and body composition, such as visceral fat area and percent body fat, were excluded in the final prediction model. Compared with existing models, the proposed prediction approach provided a rapid prediction of optimal PAP with higher accuracy. [ABSTRACT FROM AUTHOR]

Because of climate changes, natural disasters are becoming more serious. For instance, the intensity of typhoons has been increasing in recent years. Typhoons and other natural disasters have high-impact low-probability characteristics. Thus, procedures for preparing for natural disasters and increasing power system resilience are important issues. This article proposes an all-inclusive process for system operators to make decisions for enhancing power system resilience and economic value during a severe weather event. This process first considers the typhoon track, the fragility curve and the recovery time of transmission lines. After collecting these data, system simulations and a calculated resilience index are implemented according to cases with and without disaster prevention. Next, the probability threshold and calculated economic value index are obtained based on numerical weather prediction wind speeds and the cost-loss ratio. Finally, the two indexes are considered in combination to obtain the highest resilience with the greatest benefit. The proposed process helps system operators make decisions for appropriate preventive actions at the least cost. An actual Taiwan power system and a severe weather event are used as an example to demonstrate the proposed decision analysis. The simulation results indicate the feasibility of the proposed method, which can reduce potential risks caused by extreme weather events with the maximum economic benefits. [ABSTRACT FROM AUTHOR]

The R&D output and global commercialization of intellectual properties (IPs), especially patents filed in many countries, have increased dramatically over the past decade. The overwhelming growth in research and IP activities has led to a major challenge to understand and forecast technology development insights and trends. Evidence-based data analytics is essential for technology mining. The assessment of patent values is a critical aspect of technology mining, which remains a highly subjective task performed by domain experts. As businesses become globalized, subjectivity in underlying assessments of large volumes of patent documents leads to overpriced or undervalued IP sales or licensing that exposes stakeholders to legal and financial risks. Thus, the development of intelligent methods for patent valuation requires new research emphasis. This article applies a deep learning analytical method for automatic and intelligent patent value estimation. Principal component analysis (PCA) is used to identify significant patent value indicators from the given patent dataset. Then, deep neural networks (DNN) for value prediction are modeled and trained using the training set. A detailed case study of 6466 manufacturing Internet of Things (IoT) patents is analyzed to demonstrate the improved results of building PCA-preprocessed DNN models to perform patent valuations. Finally, selected higher value IoT patents owned by leading Taiwan assignees are identified and analyzed to verify the technological competitive intelligence. [ABSTRACT FROM AUTHOR]

Recently, Taiwanese Government has announced an ambitious energy policy: up to 20% of total power will be generated from renewable energy resources by 2025. Under such high-level penetration level of renewable energy resources, the typical net load curve will be definitely reshaped as the so-called duck curve. Operation modes of pumped hydro storage units in Taiwan power system will be totally changed. To tackle out this difficulty, the optimal scheduling of Taiwan power system is developed in this article for examining the coordination of highly penetrated renewable energy resources and pumped hydro storage plants under the required spinning reserve. The associated cost function and various constraints used in this optimal scheduling problem are described first. Physical characteristics of pumped hydro storage units in Taiwan will also be studied. By following the data format in the open source MATPOWER optimal scheduling tool, platform developments of this optimal scheduling program are reported. To validate the effectiveness of the proposed scheme, intensive simulations of both current scenarios and future scenarios are conducted. Simulation results indicate that existing pumped hydro storage units in Taiwan can indeed mitigate the impact of the duck-shaped net load curve in the generator scheduling of future Taiwan power system even under the worst scenario. [ABSTRACT FROM AUTHOR]

This article proposes a fault identification method that is embedded in an asset management system (AMS) to identify the faulted location based on the fault flags reported from the fault indicators (FIs). The fault identification model based on the Petri-net technology is developed using the distribution network topology generated by a geographic information system. The proposed method uses the data set of fault flags generated by FIs, the statuses of circuit breakers, the available precurrent and postcurrent measurements of FIs, and the loadings of distribution feeders and laterals. The hybrid communication system is applied to provide two-way communication between the control center and FIs. The AMS with the embedded fault identification method can dispatch the repair crews to the fault location faster to accelerate the customer service restoration. A distribution feeder of Taiwan Power Company was selected for the computer simulation to demonstrate the effectiveness of the proposed method by using the communication-capable FIs to identify the exact fault location of distribution lines after a short circuit fault occurs in a distribution system. [ABSTRACT FROM AUTHOR]

The diurnal fluctuation of acoustic propagation in a cold dome region was studied by using the output of the high-resolution Finite Volume Coastal Ocean Model. The 3-D acoustic propagation in this region was studied first, and the results suggest that the diurnal fluctuation of the ocean environment results in the fluctuation of acoustic propagation. However, the variation pattern cannot be determined with only few outputs, and comprehensive analysis was needed. Then, a feature model for the thermal front on the edge of the cold dome was proposed. By analyzing the output of the ocean model, fluctuations were found to mainly occur in the two aspects of the feature model: first, horizontal excursion of the thermal front and second, vertical motion of the isothermal lines in the cold dome. The variation pattern of acoustic propagation over the two kinds of fluctuations was quantified. Results suggest that both kinds of fluctuations of model parameters result in great transmission loss (TL) variation in some cases. Typically, acoustic propagation with high frequency and deep source experience great influence from the fluctuations. The TL versus range usually experience influence from the fluctuations of the first kind from an approximately 40-km range and from the start for the second kind. The reason is that the vertical motion of the isothermal lines of the cold dome induces variation over the temperature profile from the start until the end, whereas the horizontal excursion of the thermal front brings influence mainly over the temperature profiles near the thermal front. [ABSTRACT FROM AUTHOR]

The isolated power system in Kinmen Island, Taiwan has already implemented the special protection system (SPS) to prevent various critical contingencies that occurred during the past decade. However, when a severe N-2 contingency occurred on July 26, 2013, the SPS has not properly armed its under-frequency load shedding. After reexamining transient recorded waveforms, it was observed that the system still has sufficient spinning reserve and the coordination between the under-frequency relay and the current SPS was not well considered. In this article, the current SPS is further modified in order to avoid the occurrence of the excessive load shedding under similar N-2 contingencies. Extensive time-domain dynamical simulations are performed first to study the relationship between the amount of the load shedding and frequency deviations under these extreme N-2 contingencies. Based on these simulation results, the remediation scheme of the SPS will be proposed by modifying the existing SPS design logic and the amount of load shedding. More comprehensive input triggered states are proposed for identifying these severe N-2 contingencies. Both simulation verifications under the proposed remedial scheme and actual transient recorded waveforms under a severe contingency after commissioning this remediated SPS are provided to demonstrate the effectiveness of this proposed remediation scheme. [ABSTRACT FROM AUTHOR]

The penetration of renewable energy is gradually increasing. Therefore, power system flexibility, which is required to maintain system security and to cope with renewable generation uncertainty, becomes more important. Various technologies, such as flexible generators, demand management, energy storage, network reconfiguration, and even efficient system operations, are capable of improving power system flexibility. Nevertheless, among them, flexible generation resources can provide a direct way to enhance the power system flexibility. This article first applies the fuzzy analytic hierarchy process to calculate the flexibility index of generation resources. Then, the process of unit scheduling is implemented under various operation scenarios to investigate the relationship between the generation flexibility and the cost of unit scheduling. Finally, the required flexibility and the corresponding capacity of flexible generators under a specified penetration of renewable power generation are investigated under different operation scenarios. [ABSTRACT FROM AUTHOR]

Electric load analysis is an important task in early new stage for design of shipboard power systems (SPSs). The installation generation capacity onboard depends on the accurate result of the analysis, which significantly affects the system economic and secure operations. In the traditional electrical load analysis, the demand factor is often obtained from engineering experiences which may lead to inaccurate analysis results. In order to obtain better results, a methodology for electrical load analysis in SPSs is proposed in this article. The load analysis spreadsheet is adopted to find the power consumption of key electric loads in SPSs. The actual power consumptions of selected loads are collected by intelligent meters. The statistical analysis is then performed to find the critical parameter of each selected load in the load analysis under different operating conditions based on the power measurement of field test and load analysis is performed for estimating system power demand accordingly. The large practical maritime vessel power systems are selected for numerical analysis to ensure the performance of the proposed method. The proposed method has been adopted by local ship builders in Taiwan to better understand the load characteristics in shipboard power systems to support various functions of system planning and operation in a more effective manner. [ABSTRACT FROM AUTHOR]

Erectile dysfunction (ED) affects millions of men worldwide. Men with ED generally complain failure to attain or maintain an adequate erection during sexual activity. The prevalence of ED is strongly correlated with age, affecting about 40% of men at age 40 and nearly 70% at age 70. A variety of chronic diseases, including diabetes, ischemic heart disease, congestive heart failure, hypertension, depression, chronic renal failure, obstructive sleep apnea, prostate disease, gout, and sleep disorder, were reported to be associated with ED. In this study, data retrieved from a subset of the National Health Insurance Research Database of Taiwan were used for designing the clinical decision support system (CDSS) for predicting ED incidences in men. The positive cases were male patients aged 20-65 who were diagnosed with ED between January 2000 and December 2010 confirmed by at least three outpatient visits or at least one inpatient visit, while the negative cases were randomly selected from the database without a history of ED and were frequency (1:1), age, and index year matched with the ED patients. Data of a total of 2832 ED patients and 2832 non-ED patients, each consisting of 41 features including index age, 10 comorbidities, and 30 other comorbidity-related variables, were retrieved for designing the predictive models. Integrated genetic algorithm and support vector machine was adopted to design the CDSSs with two experiments of independent training and testing (ITT) conducted to verify their effectiveness. In the 1st ITT experiment, data extracted from January 2000 till December 2005 (61.51%, 1742 positive cases and 1742 negative cases) were used for training and validating and the data retrieved from January 2006 till December 2010 were used for testing (38.49%), whereas in the 2nd ITT experiment, data in the training set (77.78%) were extracted from January 2000 till Deceber 2007 and those in the testing set (22.22%) were retrieved afterward. Tenfold cross validation and three different objective functions were adopted for obtaining the optimal models with best predictive performance in the training phase. The testing results show that the CDSSs achieved a predictive performance with accuracy, sensitivity, specificity, g-mean, and area under ROC curve of 74.72%–76.65%, 72.33%–83.76%, 69.54%–77.10%, 0.7468–0.7632, and 0.766–0.817, respectively. In conclusion, the CDSSs designed based on cost-sensitive objective functions as well as salient comorbidity-related features achieve satisfactory predictive performance for predicting ED incidences. [ABSTRACT FROM AUTHOR]

Presents the front cover for this issue of the publication. [ABSTRACT FROM PUBLISHER]