Nobuyoshi Komuro

The overall energy efficiency of a multi-compressor system varies greatly depending on its operating strategy. In most industrial facilities, the strategy is still determined empirically by operators, which often fails to achieve optimal energy efficiency. The optimization of multi-compressor operation is inherently complex, as it must simultaneously consider various operational factors such as on/off combinations, type-specific flow capacities, and flow constraints. This study proposes a parallel-search simulated annealing algorithm that employs a branch-based exploration mechanism. In the proposed algorithm, multiple search branches independently explore the solution space and periodically exchange information, enabling a broader search and faster convergence than conventional simulated annealing. Simulation results show that the proposed approach reduces total power consumption by about 8% compared with existing heuristic methods while maintaining stable and consistent performance across large-scale scenarios.

This study investigates a spatial visualization method for indoor environmental conditions integrating a sensor network with computational fluid dynamics (CFD). Temperature and carbon dioxide concentration were estimated by integrating sparse sensor observations with CFD-based background fields through Optimal Interpolation (OI). We demonstrate that the proposed OI-based method outperforms conventional interpolation techniques (e.g., IDW, Kriging) in terms of reconstruction accuracy, particularly under sparse sensor configurations. By integrating CFD-derived background fields with observational data, our approach effectively mitigates the estimation errors caused by sensor scarcity, thereby enabling physically consistent spatial visualization.

Since GPS (Global Positioning System) cannot meet accuracy requirements indoors, indoor Location-Based Services (LBSs) have become increasingly important. BLE (Bluetooth Low Energy) offers cost and accuracy advantages. Typically, the position fingerprinting method is used for indoor positioning. However, due to irregular reflection and absorption, the indoor environment introduces various offsets in Bluetooth RSSI (Received Signal Strength Indicator). This study analyzed the RSSI space and proposed a pre-processing workflow to improve position estimation accuracy by correcting offsets in RSSI space for BLE fingerprinting methods using machine learning. Experiments performed using different position estimation methods showed that the corrected data achieved a 6% improvement over the filter-only result. This study also evaluated the effects of different pre-processing and post-processing filters on positioning accuracy. Experiments were also conducted using a published dataset and showed similar results.

Indoor environmental factors such as CO₂ concentration, temperature, and humidity can significantly influence individuals’ emotional states and productivity. This study continuously collected environmental data using wireless sensors and emotional data from wearable devices in an office-like remote-work setting. Machine learning models, including Random Forest and Gradient Boosting Decision Tree, were developed and interpreted using SHAP (Shapley Additive Explanations). The proposed models achieved estimation accuracies above 85%. SHAP analysis revealed that CO₂ concentration, temperature, and humidity were influential factors in predicting pleasant or unpleasant states. These findings demonstrate the feasibility of real-time, data-driven emotion estimation and provide insights into the design of indoor environments that foster comfort and mental well-being.

JCM is an open access journal on the science and engineering of communication.

Providing Optical Wireless Communications (OWCs) is desirable for high data transmission efficiency. Intensity Modulation and Direct Detection (IM/DD) is widely adopted for its simplicity and practicality. Among various modulation schemes, Code Shift Keying (CSK) has demonstrated superior transmission efficiency compared to On-Off Keying (OOK) and Pulse Position Modulation (PPM). Prior research has shown that CSK performance can be further enhanced through parallel transmission and code concatenation techniques. However, the direct application of concatenated CSK to parallel transmission reduces the number of available code combinations as the concatenation level increases, potentially lowering modulation efficiency. This study proposes an advanced transmission scheme that integrates parallel transmission with a multi-level intensity adjustment mechanism. The proposed method preserves a high number of distinguishable transmission symbols, thereby achieving higher data transmission rates. Analytical derivations for transmission efficiency are provided for single-user scenarios, and numerical simulations validate the effectiveness of the proposed system. The key contributions of this work include mitigating symbol reduction in nonorthogonal CSK with parallel transmission and adjusting the multi-level intensity to enhance overall system performance. The results confirm that the proposed scheme significantly improves the efficiency and scalability of nonorthogonal CSK in OWC applications.

We propose an indoor location estimation method using sensors of mobile devices. First, we perform attitude estimation using each sensor. This estimation is used to estimate the attitude of the mobile device with respect to the earth. Based on the acceleration and other information obtained from the attitude estimation, we then estimate the step detection, step length, and direction of the step. Finally, the location is calculated using all the estimation results. To eliminate the need to hold the mobile device in place during the estimation process, the method is configured so that estimates may be performed while walking, while looking at the screen, and while walking and holding the device in one hand. As the proposed method does not use indoor location fingerprinting or machine learning, real-time estimation can be performed. Although the accuracy could be higher, our experimental results show that the proposed method is able to effectively estimate the location and walking trajectory.

This paper presents a method for estimating arousal and emotional valence levels using non-contact environmental sensing, addressing challenges such as discomfort from long-term device wear and privacy concerns associated with facial image analysis. We employed environmental data to develop machine learning models, including Random Forest, Gradient Boosting Decision Trees, and the deep learning model CNN-LSTM, and evaluated their accuracy in estimating emotional states. The results indicate that decision tree-based methods, particularly Random Forest, are highly effective for estimating emotional states from environmental data.