Towards energy independence at KENTECH: A comprehensive microgrid implementation roadmap.

This paper introduces a comprehensive microgrid roadmap for the Korea Institute of Energy Technology (KENTECH), an energy specialized institute in South Korea, aligning with the country's overarching objective of achieving carbon neutrality by the year 2050. The roadmap outlines the integration of diverse energy resources-primarily renewables-to enhance sustainability and energy efficiency on campus. The paper also describes key elements for achieving autonomous energy operations through advanced technologies such as energy management systems, network gateways for system interoperability, static transfer switches, intelligent electronic devices, and power condition systems.

Improving Depression Severity Prediction from Passive Sensing: Symptom-Profiling Approach.

Depression is a significant mental health issue that profoundly impacts people's lives. Diagnosing depression often involves interviews with mental health professionals and surveys, which can become cumbersome when administered continuously. Digital phenotyping offers an innovative approach for detecting and monitoring depression without requiring active user involvement.

STDD: Short-Term Depression Detection with Passive Sensing.

It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, , , , , and and extracted features related to each symptom cluster from mobile devices' sensors.

Smombie Guardian: We watch for potential obstacles while you are walking and conducting smartphone activities.

With the growing dependence on smartphones for everyday activities, a large number of pedestrians nowadays are constantly fixated on their smartphone screens, and hence are susceptible to walking off pavements or colliding with other pedestrians. Reduced attention and situational awareness can render smartphone-occupied users, or smombies, oblivious to potential risks when using their smartphones while walking or driving. In this paper, we introduce a smartphone application, called Smombie Guardian, that detects obstacles and alerts smombies as they walk while viewing their smartphone screens to prevent potential collisions.