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Resolution Enhancement Techniques (RETs) in optical lithography have become essential for achieving the continuous shrinkage of technology nodes. The primary techniques in RETs include source mask optimization. Generally, lithography simulation, which estimates the image formed on wafers, is utilized in RETs. To optimize the light source and mask, optical conditions such as the light-source shape, numerical aperture, and exposure wavelength need to be adjusted in the lithography simulation. However, there is a shortage of open lithography simulation tools that cover various optical conditions. In academia, the study of source optimization in optical lithography is challenging. In this paper, we present the development of a lithography simulation tool set for source mask optimization. In our lithography simulation tool set, users can set optical conditions such as the light-source shape, mask shape, numerical aperture, and exposure wavelength. One function in our lithography simulation tool set calculates light intensity using the Transmission Cross Coefficient (TCC) model, while another function calculates it using the Sum of Coherent System (SOCS) for the specified optical conditions. In experiments, we verified the accuracy of our lithography simulation tool set and evaluated its execution time.

When using underwater optical wireless communication in areas close to human habitats— such as shallow sea areas—specifications for highly-secure, large-capacity optical transceivers are required. Real-time transmission of 850 nm, direct-current optical orthogonal frequency division multiplexing signals for full-duplex underwater invisible light communication has been achieved. We experimentally confirmed that subcarrier adaptive modulation could transmit at maximum capacity depending on the transmission distance, while changing the transmission distance in shallow seawater channels. We confirmed that there was no disturbing influence due to sunlight by using a honeycomb structure for sunlight shielding. Moreover, we found that the effect of disruption caused by the sea surface vibrating due to the 3 m/s wind speed did not affect the signal quality. 4K video streaming is also done on a 1.2 m underwater channel transmission. To the best of our knowledge, this is the first report of full-duplex transmission of invisible-band underwater optical wireless communication for shallow waters.

In dual-carrier (DC)-paired fiber optic long-reach transmission, which extends a point-to-multipoint coherent system to a cascaded reconfigurable optical add-drop multiplexing (ROADM) system, a narrow passband causes severe performance degradation of the edge subcarriers. In this study, we proposed two-dimensional frequency and polarization domain coding and multicarrier-frequency control for cascaded ROADM filtering of robust multicarrier transmissions at a single wavelength. The proposed coding scheme reduced fiber nonlinearity, polarization-dependent loss (PDL), and passband narrowing (PBN) simultaneously. The multicarrier frequency control algorithm, which feeds back the received signal quality at multiple points, reduced PBN owing to long-term laser frequency drifts. With the proposed coding, the Q value of 56 Gbit/s × DC-paired dual-polarized (DP)-4QAM and 112 Gbit/s × DC-paired DP-16QAM signals improved by 1 dB and 0.2 dB, respectively, compared to that when no coding was used. Furthermore, the long-term frequency control emulated through the emulation model of laser frequency drifts and the performance degradation of average Q value were less than 0.4 dB and 0.1 dB, respectively. To the best of our knowledge, no other study has evaluated point-to-multipoint transmission over ROADM-induced optical filtering systems that apply frequency and polarization domain coding and multicarrier frequency control for DC-paired DP-M-QAM signals. The proposed methods enable stable operation while suppressing the effects of PDL and PBN, which are problems in multicarrier transmission, in long-reach optical fiber transmission systems that integrate access and metro networks.

The use of electric vehicles (EVs) and photovoltaics (PV) is increasing worldwide. Transportation networks require the effective use of renewable energy (RE) for EVs, whereas power networks require local consumption of PV energy, mainly at the initiative of local governments. Although many previous studies have addressed these requirements using private EVs as mobile storages, their uncertainty and uncontrollability remain highly problematic. Therefore, our previous studies focused on electric buses because of their high controllability and certainty. These studies involved the development and evaluation of two independent minimization problems—kilowatts (KW) and kilowatt-hours (KWH) of surplus RE—as a charging schedule optimization method using mixed integer linear programming. However, the feasibility of simultaneously minimizing KW and KWH still presented technical problems. This study aims to extend and generalize the method to the simultaneous minimization of KW and KWH of the PV-derived reverse power flow (RPF). With this multiobjective optimization, the KW peak-cut of the RPF improves the hosting capacity and increases the availability of connectable RE resources, whereas the minimization of KWH promotes the local consumption of RE and decarbonization of public transportation. Two simulations using detailed data of actual bus operations and actual power flow data for a real city confirmed the feasibility of simultaneously optimizing KW and KWH and the relationship between these indicators and number of EV chargers. The optimized charging of the 17 electric buses achieved a maximum of 211.4 kW peak-cut and 1318.4 kWh RE-RPF absorption, reducing CO2 emissions by 495.7 kg/day.

In recent years, several studies have been conducted on the dynamic control of traffic signal durations using deep reinforcement learning with the aim of reducing traffic congestion. The unique advantages of independent control of traffic signals include reduction in the cost of information transmission and stable control without being affected by the failure of other traffic signals. However, conventional deep reinforcement learning methods such as Deep Q-Network may degrade the learning performance in a multi-agent environment where there are multiple traffic signals in the environment. Therefore, we propose a traffic light control system based on the dual targeting algorithm, which incorporates reinforcement of successful experiences in multi-agent environments, with the aim of realizing a better traffic light control system. The experimental results in a multi-agent environment using a traffic flow simulator based on simulation of urban mobility (SUMO) show that the proposed traffic light control system reduces the waiting time at traffic lights by 33% compared to a conventional traffic light control system using deep reinforcement learning. In the future works, we aim to apply this research to traffic light control systems in real environments.

The challenge for next-generation underwater optical wireless communication systems is to develop optical transceivers that can operate with low power consumption by maximizing the transmission capacity according to the transmission distance between transmitters and receivers. This study proposes an underwater wireless optical communication (UWOC) system using an optical transceiver with an optimum transmission rate for the deep sea with near-pure water properties. As a method for actualizing an optical transceiver with an optimum transmission rate in a UWOC system, time-domain hybrid pulse amplitude modulation (PAM) (TDHP) using a transmission rate and distance-adaptive intensity modulation/direct detection optical transceiver is considered. In the TDHP method, variable transmission capacity is actualized while changing the generation ratio of two intensity-modulated signals with different noise immunities in the time domain. Three different color laser diodes (LDs), red, blue, and green are used in an underwater channel transmission transceiver that comprises the LD and a photodiode. The maximum transmission distance while changing the incidence of PAM 2 and PAM 4 signals that calibrate the TDHP in a pure transmission line and how the maximum transmission distance changes when the optical transmitter/receiver spatial optical system is altered from the optimum conditions are clarified based on numerical calculation and simulation. To the best knowledge of the authors, there is no other research on data-rate and distance adaptive UWOC system that applies the TDHP signal with power optimization between two modulation formats.

In recent years, home energy management systems (HEMS), which enable the automatic control of electrical equipment and home appliances, have been attracting attention as a method for saving electricity at home. HEMS achieve energy saving by visualizing energy consumption at home and controlling energy consuming equipment such as air conditioners. The optimum control law is difficult to attain, owing to uncertainties related to power demand and power supply from the electrical equipment. Deep reinforcement learning has been used to address energy optimization problems for home environments. However, in HEMS, several components such as heating, ventilation, and air conditioning (HVAC) systems, storage batteries, and electric water heaters are simultaneously controlled, and therefore, the action space becomes extremely large. Therefore, it may not be feasible to fully learn the rare experience using traditional deep reinforcement learning methods due to the large size of the state-action space and slow propagation of delayed rewards. In this study, we propose an energy management algorithm that uses the Dual Targeting Algorithm to strongly learn the experience of acquiring high returns using the quick propagation of delayed rewards via multistep returns. The proposed energy management algorithm is applied to a HEMS learning experiment to control a storage battery and an HVAC system, and its performance is compared to that of a Deep Deterministic Policy Gradient-based energy management system. As a result, it is confirmed that the proposed method can reduce the number of hours deviating from the comfort temperature range by about 17% compared to the existing method.