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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]

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]

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]

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]

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]

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]